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% generated by bibtexbrowser % % Encoding: UTF-8 @article{KuschkdGRC17, author = {G. Kuschk and P. d'Angelo and D. Gaudrie and P. Reinartz and D. Cremers}, title = {Spatially Regularized Fusion of Multiresolution Digital Surface Models}, journal = {{IEEE} Trans. Geosci. Remote. Sens.}, volume = {55}, number = {3}, pages = {1477--1488}, year = {2017}, } @article{CremersLV17, author = {D. Cremers and L. Leal-Taixé and R. Vidal}, title = {Deep Learning for Computer Vision (Dagstuhl Seminar 17391)}, journal = {Dagstuhl Reports}, volume = {7}, number = {9}, pages = {109--125}, year = {2017}, } @incollection{Cremers15, author = {D. Cremers}, editor = {O. Scherzer}, title = {Image Segmentation with Shape Priors: Explicit Versus Implicit Representations}, booktitle = {Handbook of Mathematical Methods in Imaging}, pages = {1909--1944}, publisher = {Springer}, year = {2015}, } @inproceedings{CosmoABTRC16, author = {L. Cosmo and A. Albarelli and F. Bergamasco and A. Torsello and E. Rodolà and D. Cremers}, title = {A game-theoretical approach for joint matching of multiple feature throughout unordered images}, booktitle = {23rd International Conference on Pattern Recognition, {ICPR} 2016, Canc{\'{u}}n, Mexico, December 4-8, 2016}, pages = {3715--3720}, publisher = {{IEEE}}, year = {2016}, } @inproceedings{moeller-et-al-iccv15, author = {M. Moeller and J. Diebold and G. Gilboa and D. Cremers}, title = {Learning Nonlinear Spectral Filters for Color Image Reconstruction}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2015}, keywords = {diebold}, } @inproceedings{Benning0N0CGS17, author = {M. Benning and M. Möller and R. Z. Nossek and M. Burger and D. Cremers and G. Gilboa}, editor = {F. Lauze and Y. Dong and A. Dahl}, title = {Nonlinear Spectral Image Fusion}, booktitle = {Scale Space and Variational Methods in Computer Vision - 6th International Conference, {SSVM} 2017, Kolding, Denmark, June 4-8, 2017, Proceedings}, series = {Lecture Notes in Computer Science}, volume = {10302}, pages = {41--53}, publisher = {Springer}, year = {2017}, } @inproceedings{diebold-et-al-ssvm15, author = {J. Diebold and N. Demmel and C. Hazirbas and M. Möller and D. Cremers}, title = {Interactive Multi-label Segmentation of RGB-D Images}, booktitle = {Scale Space and Variational Methods in Computer Vision (SSVM)}, year = {2015}, month = {june}, keywords = {diebold, segmentation}, doi = {10.1007/978-3-319-18461-6_24}, } @inproceedings{Toker_2022_CVPR, author = {A Toker and L Kondmann and M Weber and M Eisenberger and C Andres and J Hu and A Hoderlein and C Senaras and T Davis and D Cremers and G Marchisio and X Zhu and L Leal-Taixe}, title = {DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, titleurl = {toker-et-al-cvpr22.pdf}, year = {2022}, } @inproceedings{hazirbas-et-al-ssvm15, author = {C. Hazirbas and J. Diebold and D. Cremers}, title = {Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation}, booktitle = {Scale Space and Variational Methods in Computer Vision (SSVM)}, year = {2015}, month = {june}, keywords = {diebold, segmentation}, doi = {10.1007/978-3-319-18461-6_20}, award = {Oral Presentation}, } @article{Diebold-et-al-ijcv15, author = {J. Diebold and C. Nieuwenhuis and D. Cremers}, title = {{Midrange Geometric Interactions for Semantic Segmentation}}, subtitle = {{Constraints for Continuous Multi-label Optimization}}, journal = {International Journal of Computer Vision}, issuetitle = {{Special Issue on Graphical Models for Scene Understanding}}, volume = {117}, number = {3}, pages = {199--225}, year = {2016}, publisher = {Springer US}, issn = {0920-5691}, doi = {10.1007/s11263-015-0828-7}, url = {http://dx.doi.org/10.1007/s11263-015-0828-7}, language = {English}, titleurl = {diebold-et-al-ijcv15.pdf}, keywords = {diebold}, } @article{Diebold-et-al-jmiv15, author = {J. Diebold and S. Tari and D. Cremers}, title = {The Role of Diffusion in Figure Hunt Games}, journal = {Journal of Mathematical Imaging and Vision}, year = {2015}, pages = {108-123}, issn = {0924-9907}, volume = {52}, number = {1}, publisher = {Springer}, language = {English}, titleurl = {diebold-et-al-jmiv15.pdf}, doi = {10.1007/s10851-014-0548-6}, keywords = {diebold}, } @inproceedings{Windheuser-et-al-iccv11, author = {T. Windheuser and U. Schlickewei and F. R. Schmidt and D. Cremers}, title = {Geometrically Consistent Elastic Matching of 3D Shapes: A Linear Programming Solution}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2011}, titleurl = {WSSC_iccv11.pdf}, topic = {Shape Analysis}, } @article{Windheuser-et-al-sgp11, author = {T. Windheuser and U. Schlickewei and F. R. Schmidt and D. Cremers}, title = {Large-Scale Integer Linear Programming for Orientation-Preserving 3D Shape Matching}, journal = {Computer Graphics Forum (Proceedings Symposium Geometry Processing)}, number = {5}, volume = {30}, pages = {1471--1480}, year = {2011}, publisher = {Eurographics}, titleurl = {WSSC_sgp11.pdf}, topic = {Shape Analysis}, } @inproceedings{Aubry-et-al-dagm11, author = {M. Aubry and U. Schlickewei and D. Cremers}, title = {Pose-Consistent 3D Shape Segmentation Based on a Quantum Mechanical Feature Descriptor}, booktitle = {Pattern Recognition (Proc. DAGM)}, address = {Frankfurt, Germany}, topic = {Shape Analysis}, year = {2011}, publisher = {Springer}, titleurl = {ASC_dagm11.pdf}, } @inproceedings{SSKKC-10, author = {M. Schikora and A. Schikora and K.-H. Kogel and W. Koch and D. Cremers}, title = {Probabilistic Classification of Disease Symptoms caused by Salmonella on Arabidopsis Plants}, month = {September}, year = {2010}, booktitle = {5th IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF) }, address = {Leipzig, Germany}, titleurl = {2010_sskkc_sdf.pdf}, keywords = {biology}, } @inproceedings{Bender0C17, author = {D. Bender and W. Koch and D. Cremers}, title = {Map-based drone homing using shortcuts}, booktitle = {2017 {IEEE} International Conference on Multisensor Fusion and Integration for Intelligent Systems, {MFI} 2017, Daegu, Korea (South), November 16-18, 2017}, pages = {505--511}, publisher = {{IEEE}}, year = {2017}, } @inproceedings{SBCK-10, author = {M. Schikora and D. Bender and D. Cremers and W. Koch}, title = {Passive Multi-Object Localization and Tracking Using Bearing Data}, month = {July}, year = {2010}, booktitle = {13th International Conference on Information Fusion (FUSION)}, address = {Edinburgh, UK}, titleurl = {2010_sbck_fusion.pdf}, } @inproceedings{SBKC-10, author = {M. Schikora and D. Bender and W. Koch and D. Cremers}, title = { Multi-target multi-sensor localization and tracking using passive antenna and optical sensors on UAVs }, month = {September}, year = {2010}, booktitle = {SPIE Security + Defence}, address = {Toulouse, France}, titleurl = {2010_sbkc_spie.pdf}, } @inproceedings{Toeppe-et-al-accv10, author = {E. Toeppe and M. R. Oswald and D. Cremers and C. Rother}, title = {Image-based 3D Modeling via Cheeger Sets}, booktitle = {Asian Conference on Computer Vision}, year = {2010}, address = {Queenstown, New Zealand}, month = {nov}, titleurl = {toeppe_et_al_accv10.pdf}, award = {Received Honorable Mention Award}, topic = {Single View Reconstruction, Convex Relaxation Methods}, pages = {53-64}, keywords = {singleview, convex-relaxation}, } @article{Cremers-Kolev-pami11, author = {D. Cremers and K. Kolev}, title = {Multiview Stereo and Silhouette Consistency via Convex Functionals over Convex Domains}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2011}, volume = {33}, number = {6}, pages = {1161--1174}, titleurl = {cremers_kolev_pami11.pdf}, topic = {3D Reconstruction}, keywords = {3d-reconstruction, convex-relaxation, 3drec}, } @article{Pock-et-al-10, author = {T. Pock and D. Cremers and H. Bischof and A. Chambolle}, title = {Global Solutions of Variational Models with Convex Regularization}, journal = {SIAM Journal on Imaging Sciences}, year = {2010}, volume = {3}, number = {4}, pages = {1122--1145}, titleurl = {pock_et_al_siims10.pdf}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation}, } @inproceedings{KPC-10, author = {K. Kolev and T. Pock and D. Cremers}, title = {Anisotropic Minimal Surfaces Integrating Photoconsistency and Normal Information for Multiview Stereo}, optcrossref = {}, optkey = {}, booktitle = {European Conference on Computer Vision (ECCV)}, optpages = {}, year = {2010}, opteditor = {}, optvolume = {}, optnumber = {}, optseries = {}, address = {Heraklion, Greece}, month = {September}, optorganization = {}, optpublisher = {}, optannote = {}, titleurl = {KPC-10.pdf}, topic = {3D Reconstruction, Convex Relaxation Methods}, keywords = {3d-reconstruction,3drec, convex-relaxation}, } @inproceedings{Oswald-et-al-dagm09, author = {M. R. Oswald and E. Toeppe and K. Kolev and D. Cremers}, title = {Non-Parametric Single View Reconstruction of Curved Objects using Convex Optimization}, booktitle = {Pattern Recognition (Proc. DAGM)}, year = {2009}, address = {Jena, Germany}, month = {September}, titleurl = {otkc_dagm09.pdf}, award = {Received a DAGM Paper Award}, topic = {Single View Reconstruction, Convex Relaxation Methods}, pages = {171-180}, keywords = {singleview, convex-relaxation}, } @inproceedings{Stuehmer-et-al-dagm10, author = {J. Stühmer and S. Gumhold and D. Cremers}, title = {Real-Time Dense Geometry from a Handheld Camera}, booktitle = {Pattern Recognition (Proc. DAGM)}, pages = {11-20}, year = {2010}, address = {Darmstadt, Germany}, month = {September}, titleurl = {stuehmer_et_al_dagm10.pdf}, keywords = {3d-reconstruction, rgb-d, dense, monocular, slam, vslam}, topic = {3D Reconstruction}, } @inproceedings{Stuehmer-et-al-cvgpu10, author = {J. Stühmer and S. Gumhold and D. Cremers}, title = {Parallel Generalized Thresholding Scheme for Live Dense Geometry from a Handheld Camera}, booktitle = {ECCV Workshop on Computer Vision on GPUs (CVGPU)}, year = {2010}, address = {Heraklion, Greece}, month = {September}, keywords = {3d-reconstruction, rgb-d, dense, monocular, slam, vslam}, topic = {3D Reconstruction}, } @inproceedings{Schmidt-Cremers-dagm09, author = {F. R. Schmidt and D. Cremers}, title = {A Closed-Form Solution for Image Sequence Segmentation with Dynamical Shape Priors}, booktitle = {Pattern Recognition (Proc. DAGM)}, year = {2009}, address = {Jena, Germany}, month = {September}, titleurl = {sc09.pdf}, topic = {Shape Priors}, keywords = {shape-priors}, } @inproceedings{Schmidt-et-al-cvpr09, author = {F. R. Schmidt and E. Toeppe and D. Cremers}, title = {Efficient Planar Graph Cuts with Applications in Computer Vision}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2009}, address = {Miami, Florida}, month = {jun}, titleurl = {schmidt_et_al_cvpr09.pdf}, award = {Received a CVPR Doctoral Spotlight Award}, topic = {Shape Analysis}, keywords = {shape-analysis}, pages = {351-356}, } @inproceedings{SSC2008, author = {T. Schoenemann and F. R. Schmidt and D. Cremers}, title = {Image Segmentation with Elastic Shape Priors via Global Geodesics in Product Spaces}, booktitle = {British Machine Vision Conference (BMVC)}, year = {2008}, address = {Leeds, UK}, month = {September}, titleurl = {ssc_bmvc08.pdf}, } @book{EMMCVPR-07, title = {Energy Minimization Methods for Computer Vision and Pattern Recognition (EMMCVPR)}, year = {2007}, author = {E: S.-C. Zhu and A. Yuille and D. Cremers and Y. Wang}, volume = {4679}, series = {LNCS}, publisher = {Springer}, } @inproceedings{Unger2008_TVSeg, author = {M. Unger and T. Pock and D. Cremers and H. Bischof}, title = {TVSeg - Interactive Total Variation Based Image Segmentation}, optcrossref = {}, optkey = {}, booktitle = {British Machine Vision Conference (BMVC)}, optpages = {}, year = {2008}, opteditor = {}, optvolume = {}, optnumber = {}, optseries = {}, address = {Leeds, UK}, month = {September}, optorganization = {}, optpublisher = {}, optannote = {}, titleurl = {seg_bmvc08.pdf}, } @inproceedings{Pock2008_Convex, author = {T. Pock and T. Schoenemann and G. Graber and H. Bischof and D. Cremers}, title = {A Convex Formulation of Continuous Multi-Label Problems}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2008}, address = {Marseille, France}, month = {October}, titleurl = {pock_et_al_eccv08.pdf}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation}, } @inproceedings{Pock-et-al-09, author = {T. Pock and A. Chambolle and H. Bischof and D. Cremers}, title = {A Convex Relaxation Approach for Computing Minimal Partitions}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2009}, address = {Miami, Florida}, titleurl = {pock_et_al_cvpr09.pdf}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation, medical imaging}, } @article{Chambolle-et-al-siims12, author = {A. Chambolle and D. Cremers and T. Pock}, title = {A Convex Approach to Minimal Partitions}, journal = {SIAM Journal on Imaging Sciences}, year = {2012}, volume = {5}, number = {4}, pages = {1113--1158}, titleurl = {Chambolle_et_al_siims12.pdf}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation}, } @inproceedings{Kolev-Cremers-09, author = {K. Kolev and D. Cremers}, title = {Continuous Ratio Optimization via Convex Relaxation with Applications to Multiview 3D Reconstruction}, optcrossref = {}, optkey = {}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, optpages = {}, year = {2009}, opteditor = {}, optvolume = {}, optnumber = {}, optseries = {}, address = {Miami, Florida}, titleurl = {Kolev-Cremers-09.pdf}, topic = {3D Reconstruction}, keywords = {3d-reconstruction, convex-relaxation}, } @inproceedings{Trobin2008_Fusion, author = {W. Trobin and T. Pock and D. Cremers and H. Bischof}, title = {Continuous Energy Minimization via Repeated Binary Fusion}, optcrossref = {}, optkey = {}, booktitle = {European Conference on Computer Vision (ECCV)}, optpages = {}, year = {2008}, opteditor = {}, optvolume = {}, optnumber = {}, optseries = {}, address = {Marseille, France}, month = {October}, optorganization = {}, optpublisher = {}, optannote = {}, titleurl = {trobin_eccv2008.pdf}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation}, } @inproceedings{KC-08, author = {K. Kolev and D. Cremers}, title = {Integration of Multiview Stereo and Silhouettes via Convex Functionals on Convex Domains}, optcrossref = {}, optkey = {}, booktitle = {European Conference on Computer Vision (ECCV)}, optpages = {}, year = {2008}, opteditor = {}, optvolume = {}, optnumber = {}, optseries = {}, address = {Marseille, France}, month = {October}, optorganization = {}, optpublisher = {}, optannote = {}, titleurl = {KC-08.pdf}, topic = {3D Reconstruction}, keywords = {3d-reconstruction, convex-relaxation}, } @inproceedings{Wedel-et-al-08, author = {A. Wedel and C. Rabe and T. Vaudrey and T. Brox and U. Franke and D. Cremers}, title = {Efficient Dense Scene Flow from Sparse or Dense Stereo Data}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2008}, address = {Marseille, France}, month = {October}, titleurl = {wedel_et_al_eccv08.pdf}, topic = {Motion}, keywords = {optical-flow,scene-flow}, } @inproceedings{Wedel-et-al-ivcnz08, author = {A. Wedel and T. Pock and J. Braun and U. Franke and D. Cremers}, title = {Duality TV-L1 Flow with Fundamental Matrix Prior}, booktitle = {Image Vision and Computing}, year = {2008}, address = {Auckland, New Zealand}, month = {November}, titleurl = {wedel_et_al_ivcnz08.pdf}, topic = {Motion}, keywords = {optical-flow}, } @inproceedings{KSKSC-08, author = {M. Klodt and T. Schoenemann and K. Kolev and M. Schikora and D. Cremers}, title = {An Experimental Comparison of Discrete and Continuous Shape Optimization Methods}, year = {2008}, address = {Marseille, France}, month = {October}, booktitle = {European Conference on Computer Vision (ECCV)}, titleurl = {KSKSC-08.pdf}, topic = {3D Reconstruction, Convex Relaxation Methods}, keywords = {3d-reconstruction, convex-relaxation}, } @inproceedings{Pock-et-al-cvpr08-ws, author = {T. Pock and M. Unger and D. Cremers and H. Bischof}, title = {Fast and Exact Solution of Total Variation Models on the GPU}, optcrossref = {}, optkey = {}, booktitle = {CVPR Workshop on Visual Computer Vision on GPU's}, optpages = {}, year = {2008}, opteditor = {}, optvolume = {}, optnumber = {}, optseries = {}, optaddress = {Anchorage, Alaska}, month = {June}, optorganization = {}, optpublisher = {}, titleurl = {cvpr2008ws.pdf}, optannote = {}, keywords = {medical imaging}, } @inproceedings{WedelDagstuhlOF, author = {A. Wedel and T. Pock and C. Zach and D. Cremers and H. Bischof}, title = {An Improved Algorithm for {TV-L1} Optical Flow}, month = {September}, year = {2008}, booktitle = {Proc. of the Dagstuhl Motion Workshop}, series = {LNCS}, publisher = {Springer}, titleurl = {DagstuhlOpticalFlowChapter.pdf}, keywords = {optical-flow}, } @inproceedings{Trobin-et-al-dagm08, author = {W. Trobin and T. Pock and D. Cremers and H. Bischof}, title = {An Unbiased Second-Order Prior for High-Accuracy Motion Estimation}, booktitle = {Pattern Recognition (Proc. DAGM)}, year = {2008}, series = {LNCS}, address = {Munich, Germany}, month = {jun}, publisher = {Springer}, titleurl = {trobin_et_al_dagm08.pdf}, keywords = {optic-flow}, copyright = {{Springer-Verlag Berlin Heidelberg 2007}}, } @inproceedings{Cremers-et-al-cvpr08, author = {D. Cremers and F. R. Schmidt and F. Barthel}, title = {Shape Priors in Variational Image Segmentation: Convexity, Lipschitz Continuity and Globally Optimal Solutions}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2008}, address = {Anchorage, Alaska}, month = {jun}, keywords = {shape-priors, convex-relaxation}, titleurl = {cremers_et_al_cvpr08.pdf}, topic = {Shape Priors, Convex Relaxation Methods}, } @inproceedings{Rosenhahn-et-al-cvpr08, author = {B. Rosenhahn and C. Schmaltz and T. Brox and J. Weickert and D. Cremers and H.-P. Seidel}, title = {Markerless Motion Capture of Man-Machine Interaction}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2008}, address = {Anchorage, Alaska}, month = {jun}, titleurl = {rosenhahn_et_al_cvpr08.pdf}, keywords = {tracking}, topic = {3DTracking}, } @inproceedings{Schoenemann-Cremers-08c, author = {T. Schoenemann and D. Cremers}, title = {Matching Non-rigidly Deformable Shapes Across Images: A Globally Optimal Solution}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2008}, address = {Anchorage, Alaska}, month = {jun}, keywords = {Motion}, titleurl = {deform_cvpr08.pdf}, topic = {Segmentation}, } @inproceedings{Schoenemann-Cremers-08b, author = {T. Schoenemann and D. Cremers}, title = {Globally Optimal Shape-based Tracking in Real-time}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2008}, address = {Anchorage, Alaska}, month = {jun}, keywords = {Motion}, titleurl = {rt_cvpr08.pdf}, topic = {Segmentation}, } @inproceedings{Schoenemann-Cremers-08a, author = {T. Schoenemann and D. Cremers}, title = {High Resolution Motion Layer Decomposition using Dual-space Graph Cuts}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2008}, address = {Anchorage, Alaska}, month = {jun}, keywords = {Motion}, titleurl = {srlayer_cvpr08.pdf}, topic = {Motion}, } @inproceedings{Rosenhahn-et-al-rv08, author = {B. Rosenhahn and T. Brox and D. Cremers and H.-P. Seidel}, title = {Modeling and Tracking Line-Constrained Mechanical Systems}, booktitle = {2nd Workshop on Robot Vision}, editor = {G. Sommer and R. Klette}, year = {2008}, series = {LNCS}, volume = {4931}, pages = {98--110}, titleurl = {Rosenhahn_et_al_robvis08.pdf}, topic = {3DTracking}, keywords = {tracking}, } @inproceedings{KKBC-07, author = {K. Kolev and M. Klodt and T. Brox and D. Cremers}, title = {Propagated Photoconsistency and Convexity in Variational Multiview 3D Reconstruction}, booktitle = {Workshop on Photometric Analysis for Computer Vision}, year = {2007}, address = {Rio de Janeiro, Brazil}, month = {oct}, titleurl = {KKBC-07.pdf}, topic = {3D Reconstruction}, keywords = {3d-reconstruction, convex-relaxation}, } @inproceedings{KKBEC-07, author = {K. Kolev and M. Klodt and T. Brox and S. Esedoglu and D. Cremers}, title = {Continuous Global Optimization in Multiview 3D Reconstruction}, booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, year = {2007}, series = {LNCS}, volume = {4679}, pages = {441--452}, address = {Ezhou, China}, month = {aug}, publisher = {Springer}, titleurl = {KKBEC-07.pdf}, topic = {3D Reconstruction}, copyright = {{Springer-Verlag Berlin Heidelberg 2007}}, keywords = {3d-reconstruction, convex-relaxation}, } @book{wedel-cremers-11, author = {A. Wedel and D. Cremers}, title = {Stereoscopic Scene Flow for 3D Motion Analysis}, publisher = {Springer}, year = {2011}, keywords = {optic-flow,scene-flow}, doi = {http://www.springer.com/computer/image+processing/book/978-0-85729-964-2}, } @techreport{CCP_08, author = {A. Chambolle and D. Cremers and T. Pock}, title = {A Convex Approach for Computing Minimal Partitions}, institution = {Dept. of Computer Science, University of Bonn}, type = {Technical report}, number = {TR-2008-05}, address = {Bonn, Germany}, month = {nov}, titleurl = {minimal_partitions_08.pdf}, year = {2008}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation}, } @techreport{BKC07, author = {T. Brox and O. Kleinschmidt and D. Cremers}, title = {Iterated and Efficient Nonlocal Means for Denoising of Textural Patterns}, institution = {Dept. of Computer Science, University of Bonn}, type = {Technical report}, number = {TR-2007-04}, address = {Bonn, Germany}, month = {aug}, year = {2007}, } @article{BKC08, author = {T. Brox and O. Kleinschmidt and D. Cremers}, title = {Efficient Nonlocal Means for Denoising of Textural Patterns}, journal = {IEEE Transactions on Image Processing}, volume = {17}, number = {7}, pages = {1083--1092}, month = {jul}, year = {2008}, titleurl = {brox_kleinschmidt_cremers_tip08.pdf}, } @inproceedings{Kleinschmidt-et-al-08, author = {O. Kleinschmidt and T. Brox and D. Cremers}, title = {Nonlocal texpaperture filtering with efficient tree structures and invariant patch similarity measures}, booktitle = {Int. Workshop on Local and Nonlocal Approximation}, month = {aug}, year = {2008}, address = {Lausanne, Switzerland}, titleurl = {kleinschmidt_et_al_lnla08.pdf}, } @inproceedings{BRCS07, author = {T. Brox and B. Rosenhahn and D. Cremers and H.-P. Seidel}, title = {Nonparametric density estimation with adaptive anisotropic kernels for human motion tracking}, booktitle = {Proc. 2nd International Workshop on Human Motion}, editor = {A. Elgammal and B. Rosenhahn and R. Klette}, address = {Rio de Janeiro, Brazil}, series = {LNCS}, volume = {4814}, publisher = {Springer}, month = {oct}, year = {2007}, pages = {152-165}, topic = {3DTracking}, titleurl = {BroxICCVHMWS07.pdf}, keywords = {tracking}, } @inproceedings{Schoenemann-Cremers-07a, author = {T. Schoenemann and D. Cremers}, title = {Globally Optimal Image Segmentation with an Elastic Shape Prior}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2007}, address = {Rio de Janeiro, Brazil}, month = {oct}, keywords = {shape-priors}, titleurl = {shape_iccv07.pdf}, topic = {Shape, Segmentation, Graph, Shape Priors}, } @inproceedings{Schoenemann-Cremers-07b, author = {T. Schoenemann and D. Cremers}, title = {Introducing Curvature into Globally Optimal Image Segmentation: Minimum Ratio Cycles on Product Graphs}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2007}, address = {Rio de Janeiro, Brazil}, month = {oct}, titleurl = {curv_iccv07.pdf}, topic = {Segmentation, Graph}, } @inproceedings{SFC-07, author = {F. R. Schmidt and D Farin and D. Cremers}, title = {Fast Matching of Planar Shapes in Sub-cubic Runtime}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2007}, address = {Rio de Janeiro, Brazil}, month = {oct}, keywords = {shape-analysis}, titleurl = {SFC-07.pdf}, topic = {Shape Analysis}, } @inproceedings{STCB-07a, author = {F. R. Schmidt and E. Toeppe and D. Cremers and Y. Boykov}, title = {Intrinsic Mean for Semimetrical Shape Retrieval via Graph Cuts}, booktitle = {Pattern Recognition (Proc. DAGM)}, year = {2007}, series = {LNCS}, volume = {4713}, pages = {446--455}, address = {Heidelberg, Germany}, month = {sep}, publisher = {Springer}, keywords = {shape-analysis}, titleurl = {STCB-07a.pdf}, topic = {Shape Analysis}, copyright = {{Springer-Verlag Berlin Heidelberg 2007}}, } @inproceedings{WSBC07, author = {A. Wedel and T. Schoenemann and T. Brox and D. Cremers}, title = {WarpCut - Fast obstacle segmentation in monocular video}, booktitle = {Pattern Recognition (Proc. DAGM)}, address = {Heidelberg, Germany}, series = {LNCS}, publisher = {Springer}, month = {sep}, year = {2007}, titleurl = {wedel_dagm07.pdf}, topic = {ObstacleSegmentation}, copyright = {{Springer-Verlag Berlin Heidelberg 2007}}, } @inproceedings{SRBC+07a, author = {C. Schmaltz and B. Rosenhahn and T. Brox and D. Cremers and J. Weickert and L. Wietzke and G. Sommer}, title = {Occlusion Modeling by Tracking Multiple Objects}, booktitle = {Pattern Recognition (Proc. DAGM)}, address = {Heidelberg, Germany}, series = {LNCS}, publisher = {Springer}, month = {sep}, year = {2007}, topic = {3DTracking}, keywords = {tracking}, titleurl = {schmaltz_et_al_dagm07.pdf}, } @inproceedings{RBCS07, author = {B. Rosenhahn and T. Brox and D. Cremers and H.-P. Seidel}, title = {Online smoothing for markerless motion capture}, booktitle = {Pattern Recognition (Proc. DAGM)}, address = {Heidelberg, Germany}, series = {LNCS}, publisher = {Springer}, month = {sep}, year = {2007}, topic = {3DTracking}, keywords = {tracking}, titleurl = {Rosenhahn_et_al_dagm07.pdf}, } @inproceedings{STCB-07, author = {F. R. Schmidt and E. Toeppe and D. Cremers and Y. Boykov}, title = {Efficient Shape Matching via Graph Cuts}, booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, year = {2007}, series = {LNCS}, volume = {4679}, pages = {39--54}, address = {Ezhou, China}, month = {aug}, publisher = {Springer}, keywords = {shape-analysis}, titleurl = {STCB-07.pdf}, topic = {Shape Analysis}, copyright = {{Springer-Verlag Berlin Heidelberg 2007}}, } @inproceedings{Cremers07, author = {D. Cremers}, title = {Nonlinear Dynamical Shape Priors for Level Set Segmentation}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2007}, titleurl = {cremers_cvpr07.pdf}, keywords = {shape-priors}, topic = {Level Sets, Tracking, Shape Priors, Segmentation}, } @article{Cremers-jsc08, author = {D. Cremers}, title = {Nonlinear Dynamical Shape Priors for Level Set Segmentation}, journal = {Journal of Scientific Computing}, volume = {35}, number = {2-3}, pages = {132--143}, year = {2008}, month = {jun}, titleurl = {cremers_jsc08.pdf}, keywords = {shape-priors}, topic = {Level Sets, Tracking, Shape Priors, Segmentation}, } @incollection{BRC07, author = {T. Brox and B. Rosenhahn and D. Cremers}, title = {Contours, optic flow, and prior knowledge: cues for capturing {3D} human motion in videos}, booktitle = {Human Motion - Understanding, Modeling, Capture, and Animation}, publisher = {Springer}, year = {2007}, titleurl = {Brox_et_al_HMBook07.pdf}, keywords = {tracking}, topic = {3DTracking, Statistics, Pose Estimation, Machine Learning, Tracking}, } @incollection{Chambolle-et-al-10, author = {A. Chambolle and V. Caselles and D. Cremers and M. Novaga and T. Pock}, title = {An Introduction to Total Variation for Image Analysis}, booktitle = {Theoretical Foundations and Numerical Methods for Sparse Recovery}, publisher = {De Gruyter}, year = {2010}, titleurl = {Chambolle_et_al_10.pdf}, } @incollection{Cremers-et-al-11, author = {D. Cremers and T. Pock and K. Kolev and A. Chambolle}, title = {Convex Relaxation Techniques for Segmentation, Stereo and Multiview Reconstruction}, booktitle = {Markov Random Fields for Vision and Image Processing}, publisher = {MIT Press}, year = {2011}, titleurl = {cremers_et_al_mrf2011.pdf}, keywords = {3d-reconstruction, convex-relaxation}, topic = {Convex Relaxation Methods}, } @incollection{Cremers-11, author = {D. Cremers}, title = {Image Segmentation with Shape Priors: Explicit Versus Implicit Representations}, booktitle = {Handbook of Mathematical Methods in Imaging}, publisher = {Springer}, year = {2011}, pages = {1453-1487}, titleurl = {cremers_handbook2011.pdf}, keywords = {shape, segmentation}, topic = {Shape}, } @incollection{Klodt-et-al-13, author = {M. Klodt and F. Steinbruecker and D. Cremers}, title = {Moment Constraints in Convex Optimization for Segmentation and Tracking}, booktitle = {Advanced Topics in Computer Vision}, publisher = {Springer}, year = {2013}, titleurl = {Klodt-et-al-13.pdf}, keywords = {segmentation, convex-relaxation, medical imaging}, topic = {Convex Relaxation Methods, Segmentation}, } @inproceedings{BC07a, author = {T. Brox and D. Cremers}, title = {On the Statistical Interpretation of the Piecewise Smooth {M}umford-{S}hah Functional}, booktitle = {Proc. International Conference on Scale Space and Variational Methods in Computer Vision}, editor = {F. Sgallari and A. Murli and N. Paragios}, series = {LNCS}, volume = {4485}, pages = {203--213}, publisher = {Springer}, year = {2007}, month = {may}, address = {Ischia, Italy}, titleurl = {brox_cremers_ssvm07_seg.pdf}, topic = {Statistics, Segmentation}, copyright = {{Springer-Verlag Berlin Heidelberg 2007}}, } @inproceedings{BC07b, author = {T. Brox and D. Cremers}, title = {Iterated Nonlocal Means for Texture Restoration}, booktitle = {Proc. International Conference on Scale Space and Variational Methods in Computer Vision}, editor = {F. Sgallari and A. Murli and N. Paragios}, series = {LNCS}, volume = {4485}, pages = {13--24}, publisher = {Springer}, year = {2007}, month = {may}, address = {Ischia, Italy}, titleurl = {brox_cremers_ssvm07_denoising.pdf}, topic = {Image Enhancement, Texture}, copyright = {{Springer-Verlag Berlin Heidelberg 2007}}, } @inproceedings{SRBC+07, author = {C. Schmaltz and B. Rosenhahn and T. Brox and D. Cremers and J. Weickert and L. Wietzke and G. Sommer}, title = {Region-based Pose Tracking}, booktitle = {Proc. 3rd Iberian Conference on Pattern Recognition and Image Analysis}, series = {LNCS}, publisher = {Springer}, year = {2007}, month = {jun}, address = {Girona, Spain}, titleurl = {schmaltz_et_al_ibpria07.pdf}, keywords = {tracking}, topic = {3DTracking, Pose Estimation, Tracking}, } @article{Brox-Cremers-ijcv09, author = {T. Brox and D. Cremers}, title = {On local region models and a statistical interpretation of the piecewise smooth Mumford-Shah functional}, journal = {International Journal of Computer Vision}, year = {2009}, volume = {84}, number = {2}, pages = {184--193}, titleurl = {brox_cremers_ijcv09.pdf}, topic = {Segmentation}, preprint = {{Shorter version of a technical report.}}, } @article{Goldluecke-et-al-ijcv14, author = {B. Goldluecke and M. Aubry and K. Kolev and D. Cremers}, title = {A Super-resolution Framework for High-Accuracy Multiview Reconstruction}, journal = {International Journal of Computer Vision}, year = {2014}, month = {jan}, volume = {106}, number = {2}, pages = {172-191}, keywords = {convex-relaxation}, titleurl = {goldluecke-et-al-ijcv14.pdf}, } @article{Wedel-et-al-ijcv11, author = {A. Wedel and T. Brox and T. Vaudrey and C. Rabe and U. Franke and D. Cremers}, title = {Stereoscopic Scene Flow Computation for 3D Motion Understanding}, journal = {International Journal of Computer Vision}, year = {2011}, volume = {95}, number = {1}, pages = {29--51}, titleurl = {wedel_et_al_ijcv11.pdf}, keywords = {optical-flow,scene-flow}, } @inproceedings{FRS-et-al-06, author = {F. R. Schmidt and M. Clausen and D. Cremers}, title = {Shape Matching by Variational Computation of Geodesics on a Manifold}, booktitle = {Pattern Recognition (Proc. DAGM)}, year = {2006}, series = {LNCS}, volume = {4174}, pages = {142--151}, address = {Berlin, Germany}, month = {sep}, publisher = {Springer}, keywords = {shape-analysis}, titleurl = {FRS-et-al-06.pdf}, topic = {Shape Analysis}, copyright = {{Springer-Verlag Berlin Heidelberg 2006}}, } @inproceedings{SC06, author = {T. Schoenemann and D. Cremers}, title = {Near Real-time Motion Segmentation using Graph Cuts}, booktitle = {Pattern Recognition (Proc. DAGM)}, year = {2006}, series = {LNCS}, volume = {4174}, pages = {455--464}, address = {Berlin, Germany}, month = {sep}, publisher = {Springer}, titleurl = {sc_dagm2006.pdf}, topic = {Motion Segmentation, Discrete Optimization, Motion}, copyright = {{Springer-Verlag Berlin Heidelberg 2006}}, } @inproceedings{Cremers-04, author = {D. Cremers}, title = {Bayesian Approaches to Motion-based Image and Video Segmentation}, booktitle = {1st Int. Workshop on Complex Motion}, year = {2004}, series = {LNCS}, volume = {3417}, pages = {106--125}, address = {Schloss Reisensburg, Germany}, month = {oct}, publisher = {Springer}, titleurl = {cremers_iwcm04.pdf}, topic = {Motion Segmentation, Level Sets, Motion}, copyright = {{Springer-Verlag Berlin Heidelberg 2004}}, } @inproceedings{BRKC06, author = {T. Brox and B. Rosenhahn and U. Kersting and D. Cremers}, title = {Nonparametric density estimation for human pose tracking}, booktitle = {Pattern Recognition (Proc. DAGM)}, editor = {K. Franke et al.}, year = {2006}, series = {LNCS}, volume = {4174}, pages = {546--555}, address = {Berlin, Germany}, month = {sep}, publisher = {Springer}, preprint = {{Revised version of Preprint No. 5-06, Department of Computer Science, University of Bonn, Germany, April 2006.}}, titleurl = {brox_dagm06}, keywords = {tracking}, topic = {3DTracking, Pose Estimation, Machine Learning, Tracking}, copyright = {{Springer-Verlag Berlin Heidelberg 2006}}, } @inproceedings{KBC06, author = {K. Kolev and T. Brox and D. Cremers}, title = {Robust variational segmentation of {3D} objects from multiple views}, booktitle = {Pattern Recognition (Proc. DAGM)}, editor = {K. Franke et al.}, year = {2006}, series = {LNCS}, volume = {4174}, pages = {688--697}, address = {Berlin, Germany}, month = {sep}, publisher = {Springer}, titleurl = {kolev_dagm06.pdf}, topic = {3D Reconstruction}, copyright = {{Springer-Verlag Berlin Heidelberg 2006}}, keywords = {3d-reconstruction}, } @inproceedings{WFKBC06, author = {A. Wedel and U. Franke and J. Klappstein and T. Brox and D. Cremers}, title = {Realtime depth estimation and obstacle detection from monocular video}, booktitle = {Pattern Recognition (Proc. DAGM)}, editor = {K. Franke et al.}, year = {2006}, series = {LNCS}, volume = {4174}, pages = {475--484}, address = {Berlin, Germany}, month = {sep}, publisher = {Springer}, titleurl = {wedel_dagm06.pdf}, topic = {ObstacleSegmentation}, copyright = {{Springer-Verlag Berlin Heidelberg 2006}}, } @incollection{Bergtholdt-et-al-06, author = {M. Bergtholdt and D. Cremers and C. Schnörr}, title = {Variational segmentation with shape priors}, booktitle = {Handbook of {M}athematical {M}odels in {C}omputer {V}ision}, publisher = {Springer}, year = {2005}, editor = {N. Paragios, Y. Chen, O. Faugeras}, topic = {Segmentation, Shape, Statistics, Machine Learning}, } @inproceedings{Boykov-et-al-06, author = {Y. Boykov and V. Kolmogorov and D. Cremers and A. Delong}, title = {An integral solution to surface evolution {PDE}s via {G}eo-{C}uts}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2006}, volume = {3953}, editor = {A. Leonardis and H. Bischof and A. Pinz}, series = {LNCS}, pages = {409--422}, address = {Graz, Austria}, month = {may}, publisher = {Springer}, copyright = {{Springer-Verlag Berlin Heidelberg 2006}}, keywords = {graph cuts, pdes, contour evolution}, titleurl = {boykov_et_al_eccv06.pdf}, topic = {Graph, Segmentation}, } @inproceedings{RBCS06, author = {B. Rosenhahn and T. Brox and D. Cremers and H.-P. Seidel}, title = {A comparison of shape matching methods for contour based pose estimation}, booktitle = {Proc. {I}nternational {W}orkshop on {C}ombinatorial {I}mage {A}nalysis}, editor = {R. Reulke and U. Eckhardt and B. Flach and U. Knauer and K. Polthier}, year = {2006}, series = {LNCS}, volume = {4040}, pages = {263--276}, address = {Berlin, Germany}, month = {jun}, publisher = {Springer}, topic = {Pose Estimation, Correspondence, Tracking}, titleurl = {rosenhahn_iwcia06.pdf}, copyright = {{Springer-Verlag Berlin Heidelberg 2006}}, } @inproceedings{BRCS06, author = {T. Brox and B. Rosenhahn and D. Cremers and H.-P. Seidel}, title = {High accuracy optical flow serves 3-{D} pose tracking: exploiting contour and flow based constraints}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2006}, editor = {A. Leonardis and H. Bischof and A. Pinz}, volume = {3952}, series = {LNCS}, pages = {98--111}, address = {Graz, Austria}, month = {may}, publisher = {Springer}, copyright = {{Springer-Verlag Berlin Heidelberg 2006}}, titleurl = {brox_eccv06_pose.pdf}, keywords = {tracking}, topic = {3DTracking, Optic Flow, Segmentation, Pose Estimation, Level Sets}, } @inproceedings{Keuchel-et-al-02, author = {J. Keuchel and C. Schnoerr and C. Schellewald and D. Cremers}, title = {Unsupervised Image Partitioning with Semidefinite Programmifng}, booktitle = {Pattern Recognition}, editor = {van Gool, L.}, publisher = {Springer}, series = {LNCS}, volume = {2449}, year = {2002}, titleurl = {keuchel_et_al_02.pdf}, address = {Z{\"u}rich}, month = {Sept.}, pages = {141--149}, keywords = {convex-relaxation}, } @inproceedings{Cremers-03, author = {D. Cremers}, title = {A variational framework for image segmentation combining motion estimation and shape regularization}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2003}, editor = {C. Dyer and P. Perona}, volume = {1}, pages = {53--58}, month = {June}, titleurl = {cvpr03.pdf}, topic = {Optic Flow, Segmentation, Motion}, } @inproceedings{Cremers-03b, author = {D. Cremers}, title = {A multiphase level set framework for variational motion segmentation}, booktitle = {Scale-{S}pace {M}ethods in {C}omputer {V}ision}, year = {2003}, editor = {L. D. Griffin and M. Lillholm}, volume = {2695}, series = {LNCS}, pages = {599--614}, address = {Isle of Skye}, publisher = {Springer}, titleurl = {cremers_scalespace03.pdf}, topic = {Segmentation, Shape, Statistics, Motion}, } @article{Cremers-06, author = {D. Cremers}, title = {Dynamical statistical shape priors for level set based tracking}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2006}, volume = {28}, number = {8}, pages = {1262--1273}, month = {aug}, keywords = {shape-priors}, titleurl = {cremers_pami06.pdf}, topic = {Level Sets, Shape Priors, Segmentation, Tracking}, } @article{Brox-et-al-pami09, author = {T. Brox and B. Rosenhahn and J. Gall and D. Cremers}, title = {Combined region- and motion-based 3D tracking of rigid and articulated objects}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2009}, volume = {32}, number = {3}, pages = {402-415}, keywords = {tracking,shape-priors}, titleurl = {brox_et_al_pami09.pdf}, topic = {Level Sets, Shape Priors, 3DTracking}, } @article{Schoenemann-Cremers-pami10, author = {T. Schoenemann and D. Cremers}, title = {A Combinatorial Solution for Model-based Image Segmentation and Real-time Tracking}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2010}, volume = {32}, number = {7}, pages = {1153--1164}, titleurl = {schoenemann_cremers_pami10.pdf}, topic = {Segmentation}, } @article{Schoenemann-Cremers-tip12, author = {T. Schoenemann and D. Cremers}, title = {A Coding Cost Framework for Super-resolution Motion Layer Decomposition}, journal = {IEEE Transactions on Image Processing}, year = {2012}, month = {March}, volume = {21}, number = {3}, pages = {1097--1110}, titleurl = {schoenemann_cremers_tip12.pdf}, topic = {Segmentation}, } @article{Schoenemann-et-al-tip11, author = {T. Schoenemann and S. Masnou and D. Cremers}, title = {The Elastic Ratio: Introducing Curvature into Ratio-Based Globally Optimal Image Segmentation}, journal = {IEEE Transactions on Image Processing}, year = {2011}, volume = {20}, number = {9}, pages = {2565--2581}, titleurl = {schoenemann_et_al_tip11.pdf}, topic = {Segmentation}, } @article{Schoenemann-et-al-ijcv12, author = {T. Schoenemann and F. Kahl and S. Masnou and D. Cremers}, title = {A linear framework for region-based image segmentation and inpainting involving curvature penalization}, journal = {International Journal of Computer Vision}, year = {2012}, month = {aug}, volume = {99}, issue = {1}, pages = {53--68}, titleurl = {schoenemann_et_al_ijcv12.pdf}, topic = {Segmentation}, keywords = {segmentation, curvature}, } @article{Cremers-07b, author = {D. Cremers}, title = {Computer Lernen Sehen}, journal = {Industrial Vision}, year = {2007}, volume = {2}, pages = {60}, titleurl = {cremers_industrial_vision_07.pdf}, } @phdthesis{Cremers-diss-online, author = {D. Cremers}, title = {Statistical shape knowledge in variational image segmentation}, school = {Department of Mathematics and Computer Science, University of Mannheim, Germany}, year = {2002}, titleurl = {cremers_dissertation.pdf}, topic = {Segmentation, Shape, Statistics, Optic Flow, Motion}, } @inproceedings{Cremers-Grady-06, author = {D. Cremers and L. Grady}, title = {Statistical priors for combinatorial optimization: efficient solutions via {G}raph {C}uts}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2006}, editor = {A. Leonardis and H. Bischof and A. Pinz}, volume = {3953}, series = {LNCS}, pages = {263--274}, address = {Graz, Austria}, month = {may}, publisher = {Springer}, copyright = {{Springer-Verlag Berlin Heidelberg 2006}}, keywords = {image-segmentation,shape,Parzen,parametric,2d cardiac ultrasound,3d ct prostate}, titleurl = {cremers_grady_eccv06.pdf}, topic = {Statistics, Texture}, } @article{Cremers-et-al-ijcv07, author = {D. Cremers and M. Rousson and R. Deriche}, title = {A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape}, journal = {International Journal of Computer Vision}, year = {2007}, volume = {72}, number = {2}, pages = {195--215}, month = {apr}, titleurl = {cremers_rousson_deriche_ijcv07.pdf}, keywords = {shape-priors, medical imaging}, topic = {Segmentation, Statistics, Shape Priors, Level Sets, Motion}, } @article{Kolev-et-al-ijcv09, author = {K. Kolev and M. Klodt and T. Brox and D. Cremers}, title = {Continuous Global Optimization in Multiview 3D Reconstruction}, journal = {International Journal of Computer Vision}, year = {2009}, month = {August}, volume = {84}, number = {1}, pages = {80--96}, titleurl = {Kolev-et-al-ijcv09.pdf}, topic = {3D Reconstruction, Convex Relaxation Methods}, keywords = {3d-reconstruction,3drec,convex-relaxation}, } @inproceedings{Cremers-et-al-06c, author = {D. Cremers and C. Guetter and C. Xu}, title = {Nonparametric priors on the space of joint intensity distributions for non-rigid multi-modal image registration}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2006}, month = {June}, pages = {1777--1783}, volume = {2}, titleurl = {cremers_et_al_cvpr06.pdf}, topic = {Optic Flow, Medical Image Analysis, Correspondence, Motion}, keywords = {medical imaging}, } @article{Cremers-Herz-02, author = {D. Cremers and A. V. M. Herz}, title = {Travelling waves of exitation in neural field models: {E}quivalence of rate descriptions and integrate-and-fire dynamics}, journal = {Neural Computation}, year = {2002}, volume = {14}, pages = {1651--1667}, number = {7}, titleurl = {nc_02.pdf}, topic = {Neural Field Models}, keywords = {biology}, } @incollection{Cremers-Kohlberger-06, author = {D. Cremers and T. Kohlberger}, title = {Probabilistic kernel {PCA} and its application to statistical shape modeling and inference}, booktitle = {Kernel {M}ethods in {B}ioengineering, {S}ignal and {I}mage {P}rocessing}, publisher = {Idea Group Inc.}, year = {2006}, editor = {G. Camps-Valls et al.}, topic = {Segmentation, Shape, Statistics, Machine Learning}, } @inproceedings{Cremers-et-al-02, author = {D. Cremers and T. Kohlberger and C. Schnörr}, title = {Nonlinear shape statistics in {M}umford--{S}hah based segmentation}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2002}, editor = {A. Heyden and others}, volume = {2351}, series = {LNCS}, pages = {93--108}, address = {Copenhagen}, month = {May}, publisher = {Springer}, titleurl = {cremers_eccv02.pdf}, keywords = {shape-priors}, topic = {Segmentation, Statistics, Shape Priors, Machine Learning}, } @inproceedings{Cremers-et-al-01b, author = {D. Cremers and T. Kohlberger and C. Schnörr}, title = {Nonlinear shape statistics via kernel spaces}, booktitle = {Pattern Recognition (Proc. DAGM)}, year = {2001}, editor = {B. Radig and S. Florczyk}, volume = {2191}, series = {LNCS}, pages = {269--276}, address = {Munich, Germany}, month = {Sept.}, publisher = {Springer}, titleurl = {dagm01_cremers.pdf}, topic = {Shape, Statistics, Machine Learning}, } @inproceedings{Keuchel-et-al-01, author = {J. Keuchel and C. Schellewald and D. Cremers and C. Schnoerr}, title = {Convex Relaxations for Binary Image Partitioning and Perceptual Grouping}, booktitle = {Pattern Recognition}, editor = {Radig, B. and Florczyk, S.}, publisher = {Springer}, series = {LNCS}, volume = {2191}, year = {2001}, award = {Received a DAGM Paper Award}, address = {Munich, Germany}, month = {Sept.}, pages = {353--360}, keywords = {convex-relaxation}, } @article{Cremers-et-al-03hd, author = {D. Cremers and T. Kohlberger and C. Schnörr}, title = {Shape {S}tatistics in {K}ernel {S}pace for {V}ariational {I}mage {S}egmentation}, journal = {Pattern {R}ecognition}, year = {2003}, volume = {36}, pages = {1929--1943}, number = {9}, award = {Awarded Best Paper of the Year 2003}, titleurl = {nonlinear_pr03.pdf}, keywords = {shape-priors}, topic = {Shape Priors, Statistics, Machine Learning, Segmentation}, } @inproceedings{Cremers-Funkalea-05, author = {D. Cremers and G. Funka-Lea}, title = {Dynamical statistical shape priors for level set based tracking}, booktitle = {Intl. {W}orkshop on {V}ariational and {L}evel {S}et {M}ethods}, year = {2005}, editor = {N. Paragios and F. Faugeras and T. Chan and C. Schn{\"o}rr}, volume = {3752}, series = {LNCS}, publisher = {Springer}, keywords = {dynamical shape priors}, titleurl = {cremers_funkalea_05.pdf}, topic = {Shape Priors, Statistics, Dynamical Systems, Segmentation, Tracking}, } @article{Cremers-Mielke-99, author = {D. Cremers and A. Mielke}, title = {Flow equations for the {H}{\'e}on-{H}eiles {H}amiltonian}, journal = {Physica D}, year = {1999}, volume = {126}, pages = {123--135}, titleurl = {2167.pdf}, topic = {Physics, Quantum Chaos}, } @inproceedings{Cremers-et-al-04b, author = {D. Cremers and S. J. Osher and S. Soatto}, title = {Kernel density estimation and intrinsic alignment for knowledge-driven segmentation: {T}eaching level sets to walk}, booktitle = {Pattern Recognition (Proc. DAGM)}, year = {2004}, editor = {C. E. Rasmussen}, volume = {3175}, series = {LNCS}, pages = {36--44}, publisher = {Springer}, titleurl = {cremers_dagm04.pdf}, keywords = {shape-priors}, topic = {Level Sets, Shape Priors, Statistics, Segmentation}, } @article{Cremers-et-al-06b, author = {D. Cremers and S. J. Osher and S. Soatto}, title = {Kernel density estimation and intrinsic alignment for shape priors in level set segmentation}, journal = {International Journal of Computer Vision}, year = {2006}, volume = {69}, number = {3}, month = {sep}, pages = {335--351}, titleurl = {cremers_osher_soatto_ijcv06.pdf}, topic = {Level Sets, Shape Priors, Statistics, Segmentation}, keywords = {medical imaging}, } @incollection{Cremers-Rousson-07, author = {D. Cremers and M. Rousson}, title = {Efficient kernel density estimation of shape and intensity priors for level set segmentation}, booktitle = {Parametric and {G}eometric {D}eformable {M}odels: {A}n application in {B}iomaterials and {M}edical {I}magery}, publisher = {Springer}, year = {2007}, editor = {J. S. Suri and A. Farag}, month = {May}, titleurl = {cremers_rousson07.pdf}, topic = {Segmentation, Shape Priors, Medical Image Analysis, Statistics}, keywords = {shape-priors, medical imaging}, } @inproceedings{Cremers-Schnoerr-02, author = {D. Cremers and C. Schnörr}, title = {Motion Competition: variational integration of motion segmentation and shape regularization}, booktitle = {Pattern Recognition (Proc. DAGM)}, year = {2002}, editor = {L. van Gool}, volume = {2449}, series = {LNCS}, pages = {472--480}, address = {Z{\"u}rich}, month = {Sept.}, publisher = {Springer}, award = {Received the Best Paper Award}, titleurl = {dagm02_cremers.pdf}, topic = {Shape, Optic Flow, Statistics, Segmentation, Motion}, } @inproceedings{Cremers-Schnoerr-02b, author = {D. Cremers and C. Schnörr}, title = {Statistical shape knowledge in variational motion segmentation}, booktitle = {1st {I}nternat. {W}orkshop on {G}enerative-{M}odel-{B}ased {V}ision}, year = {2002}, editor = {A. Pece and Y. N. Wu and R. Larsen}, address = {Copenhagen}, month = {June, 2}, publisher = {Univ. of Copenhagen}, titleurl = {cremers_gmbv02.ps.gz}, topic = {Shape, Optic Flow, Statistics, Segmentation, Motion}, keywords = {optical-flow, segmentation}, } @article{Cremers-Schnoerr-03a, author = {D. Cremers and C. Schnörr}, title = {Statistical shape knowledge in variational motion segmentation}, journal = {Image and Vision Computing}, year = {2003}, volume = {21}, pages = {77--86}, number = {1}, titleurl = {cremers_ivc.pdf}, topic = {Shape, Optic Flow, Statistics, Segmentation, Motion}, keywords = {optical-flow}, } @article{Cremers-12, author = {D. Cremers}, title = {Optimal Solutions for Semantic Image Decomposition}, journal = {Image and Vision Computing}, year = {2012}, volume = {30}, pages = {476--477}, number = {8}, titleurl = {cremers_ivc12.pdf}, keywords = {convex-relaxation, segmentation}, } @article{Chen-et-al-ivc12, author = {S. Chen and D. Cremers and R. J. Radke}, title = {Image segmentation with one shape prior - A template-based formulation}, journal = {Image and Vision Computing}, year = {2012}, volume = {30}, number = {12}, pages = {1032--1042}, titleurl = {chen-et-al-ivc12.pdf}, topic = {Shape, Segmentation}, keywords = {medical imaging}, } @inproceedings{Cremers-et-al-01, author = {D. Cremers and C. Schnörr and J. Weickert}, title = {Diffusion {S}nakes: {C}ombining statistical shape knowledge and image information in a variational framework}, booktitle = {I{EEE} {F}irst {I}nt. {W}orkshop on {V}ariational and {L}evel {S}et {M}ethods}, year = {2001}, editor = {N. Paragios}, pages = {137--144}, address = {Vancouver}, award = {Best Student Paper Award}, titleurl = {cremers_vlsm.ps.gz}, topic = {Shape, Statistics, Segmentation}, } @inproceedings{Cremers-et-al-dyn, author = {D. Cremers and C. Schnörr and J. Weickert and C. Schellewald}, title = {Learning of translation invariant shape knowledge for steering diffusion snakes}, booktitle = {Dynamische {P}erzeption}, year = {2000}, editor = {G. Baratoff and H. Neumann}, volume = {9}, series = {Proceedings on Artificial Intelligence}, pages = {117--122}, address = {Ulm Germany}, month = {Nov.}, publisher = {Infix}, titleurl = {dynperz00.ps.gz}, keywords = {shape-priors}, topic = {Shape Priors, Statistics, Segmentation}, } @inproceedings{Cremers-et-al-00, author = {D. Cremers and C. Schnörr and J. Weickert and C. Schellewald}, title = {Diffusion {S}nakes using statistical shape knowledge}, booktitle = {Algebraic {F}rames for the {P}erception-{A}ction {C}ycle}, year = {2000}, editor = {G. Sommer and Y.Y. Zeevi}, volume = {1888}, series = {LNCS}, pages = {164--174}, address = {Kiel, Germany}, month = {Sept.}, publisher = {Springer}, titleurl = {afpac2000.ps.gz}, keywords = {shape-priors}, topic = {Shape Priors, Statistics, Segmentation}, } @techreport{Cremers-et-al-TR00, author = {D. Cremers and C. Schnörr and J. Weickert and C. Schellewald}, title = {Diffusion {S}nakes using statistical shape knowledge}, institution = {Dept. of Math. and Comp. Sci., Comp. Sci. Series}, year = {2000}, type = {Technical report}, number = {11/00}, address = {University of Mannheim, Germany}, month = {Mar.}, topic = {Shape, Statistics, Segmentation}, } @inproceedings{Cremers-Soatto-03b, author = {D. Cremers and S. Soatto}, title = {A pseudo-distance for shape priors in level set segmentation}, booktitle = {I{EEE} 2nd {I}nt. {W}orkshop on {V}ariational, {G}eometric and {L}evel {S}et {M}ethods}, year = {2003}, editor = {N. Paragios}, pages = {169--176}, address = {Nice}, titleurl = {cremers_soatto_vlsm03.pdf}, keywords = {shape-priors}, topic = {Shape Priors}, } @inproceedings{Cremers-Soatto-03, author = {D. Cremers and S. Soatto}, title = {Variational space-time motion segmentation}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2003}, editor = {B. Triggs and A. Zisserman}, volume = {2}, pages = {886--892}, address = {Nice}, month = {Oct.}, titleurl = {cremers_soatto_iccv03.pdf}, topic = {Optic Flow, Level Sets, Segmentation, Motion}, keywords = {optical-flow}, } @article{Cremers-Soatto-05, author = {D. Cremers and S. Soatto}, title = {Motion Competition: {A} variational framework for piecewise parametric motion segmentation}, journal = {International Journal of Computer Vision}, year = {2005}, volume = {62}, pages = {249--265}, number = {3}, month = {May}, titleurl = {cremers_soatto_ijcv05.pdf}, topic = {Optic Flow, Level Sets, Segmentation, Motion}, keywords = {optical-flow}, } @inproceedings{Cremers-et-al-03, author = {D. Cremers and N. Sochen and C. Schnörr}, title = {Towards {R}ecognition-based {V}ariational {S}egmentation {U}sing {S}hape {P}riors and {D}ynamic {L}abeling}, booktitle = {Scale-{S}pace {M}ethods in {C}omputer {V}ision}, year = {2003}, editor = {L. D. Griffin and M. Lillholm}, volume = {2695}, series = {LNCS}, pages = {388--400}, address = {Isle of Skye}, publisher = {Springer}, titleurl = {dynamic_labeling.pdf}, keywords = {shape-priors}, topic = {Shape Priors, Level Sets, Segmentation, Recognition}, } @inproceedings{Cremers-et-al-04, author = {D. Cremers and N. Sochen and C. Schnörr}, title = {Multiphase dynamic labeling for variational recognition-driven image segmentation}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2004}, editor = {T. Pajdla and V. Hlavac}, volume = {3024}, series = {LNCS}, pages = {74--86}, publisher = {Springer}, titleurl = {cremers_eccv04.pdf}, keywords = {shape-priors}, topic = {Shape Priors, Level Sets, Segmentation, Recognition}, } @article{Cremers-et-al-06, author = {D. Cremers and N. Sochen and C. Schnörr}, title = {A multiphase dynamic labeling model for variational recognition-driven image segmentation}, journal = {International Journal of Computer Vision}, year = {2006}, volume = {66}, pages = {67--81}, month = {jan}, number = {1}, titleurl = {cremers_sochen_schnoerr_ijcv06.pdf}, topic = {Shape Priors, Level Sets, Segmentation, Recognition}, } @article{Cremers-et-al-02c, author = {D. Cremers and F. Tischhäuser and J. Weickert and C. Schnörr}, title = {Diffusion {S}nakes: {I}ntroducing statistical shape knowledge into the {M}umford--{S}hah functional}, journal = {International Journal of Computer Vision}, year = {2002}, volume = {50}, pages = {295--313}, number = {3}, titleurl = {cremers_et_al_ijcv02.pdf}, keywords = {shape-priors}, topic = {Shape Priors, Segmentation, Statistics}, } @inproceedings{Cremers-Yuille-03, author = {D. Cremers and A. L. Yuille}, title = {A generative model based approach to motion segmentation}, booktitle = {Pattern Recognition (Proc. DAGM)}, year = {2003}, editor = {B. Michaelis and G. Krell}, volume = {2781}, series = {LNCS}, pages = {313--320}, address = {Magdeburg}, month = {Sept.}, publisher = {Springer}, titleurl = {generative.pdf}, topic = {Level Sets, Optic Flow, Statistics, Segmentation, Motion}, keywords = {optical-flow}, } @inproceedings{Doretto-et-al-03, author = {G. Doretto and D. Cremers and P. Favaro and S. Soatto}, title = {Dynamic texture segmentation}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2003}, editor = {B. Triggs and A. Zisserman}, volume = {2}, pages = {1236--1242}, address = {Nice}, month = {Oct.}, titleurl = {dynamic_texture_segmentation.pdf}, topic = {Level Sets, Segmentation, Texture}, } @inproceedings{Jin-et-al-04, author = {H. Jin and D. Cremers and A. Yezzi and S. Soatto}, title = {Shedding light on stereoscopic segmentation}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2004}, editor = {L. Davis}, volume = {1}, pages = {36--42}, address = {Washington, DC}, titleurl = {jin_et_al_cvpr04.pdf}, topic = {3D Reconstruction, Level Sets, Illumination}, } @article{Jin-et-al-08, author = {H. Jin and D. Cremers and D. Wang and A. Yezzi and E. Prados and S. Soatto}, title = {3-D Reconstruction of Shaded Objects from Multiple Images Under Unknown Illumination}, journal = {International Journal of Computer Vision}, volume = {76}, number = {3}, pages = {245--256}, month = {mar}, year = {2008}, titleurl = {jin_et_al_ijcv08.pdf}, topic = {3D Reconstruction, Level Sets, Illumination}, } @article{Keuchel-et-al-03, author = {J. Keuchel and C. Schnörr and C. Schellewald and D. Cremers}, title = {Binary partitioning, perceptual grouping, and restoration with semidefinite programming}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2003}, volume = {25}, pages = {1364--1379}, number = {11}, keywords = {convex-relaxation}, titleurl = {keuchel_et_al_pami03.pdf}, } @incollection{manayCHYS06, author = {S. Manay and D. Cremers and B. W. Hong and A. Yezzi and S. Soatto}, title = {Integral Invariants and Shape Matching}, booktitle = {Statistical analysis of shapes (modeling and simulation in science, engineering and technology)}, year = {2006}, month = {May}, publisher = {Birkhauser}, pages = {137--167}, titleurl = {manayCHYS06.pdf}, } @article{Manay-et-al-06, author = {S. Manay and D. Cremers and B.-W. Hong and A. Yezzi and S. Soatto}, title = {Integral invariants for shape matching}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2006}, month = {oct}, volume = {28}, number = {10}, pages = {1602--1618}, keywords = {shape}, topic = {Shape, Correspondence}, titleurl = {manay_et_al_pami06.pdf}, } @inproceedings{Manay-et-al-05, author = {S. Manay and D. Cremers and A. J. Yezzi and S. Soatto}, title = {One-shot integral invariant shape priors for variational segmentation}, booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, volume = {3757}, editor = {A. Rangarajan and B. Vemuri and A. L. Yuille}, series = {LNCS}, year = {2005}, pages = {414--426}, keywords = {image-segmentation,shape,Dijkstra + PDE}, topic = {Segmentation, Shape, Correspondence}, } @inproceedings{Wedel-et-al-emmcvpr09, author = {A. Wedel and C. Rabe and A. Meissner and U. Franke and D. Cremers}, title = {Detection and Segmentation of Independently Moving Objects from Dense Scene Flow}, booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, volume = {5681}, editor = {D. Cremers and Y. Boykov and A. Blake and F. R. Schmidt}, series = {LNCS}, year = {2009}, keywords = {scene-flow}, titleurl = {wedel_et_al_emmcvpr09.pdf}, } @article{Wedel-et-al-its09, author = {A. Wedel and C. Rabe and H. Badino and H. Loose and U. Franke and D. Cremers}, title = {B-Spline Modeling of Road Surfaces with an Application to Free Space Estimation}, journal = {Transactions on Intelligent Transportation Systems}, year = {2009}, volume = {10}, number = {4}, pages = {572--583}, titleurl = {wedel_et_al_its09.pdf}, } @inproceedings{Rousson-Cremers-05, author = {M. Rousson and D. Cremers}, title = {Efficient kernel density estimation of shape and intensity priors for level set segmentation}, booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)}, year = {2005}, volume = {1}, pages = {757--764}, keywords = {image-segmentation,shape,Parzen,parametric,2d cardiac ultrasound,3d ct prostate, medical imaging}, titleurl = {rousson_cremers05.pdf}, topic = {Shape, Segmentation, Statistics, Level Sets}, } @inproceedings{Fluck-et-al-06, author = {O. Fluck and S. Aharon and D. Cremers and M. Rousson}, title = {GPU histogram computation}, booktitle = {ACM SIGGRAPH posters and demos}, year = {2006}, keywords = {image segmentation, Parzen, Level Sets}, titleurl = {fluck_et_al_siggraph06_abstract.pdf}, topic = {Segmentation, Statistics, Level Sets}, } @inproceedings{Cremers-et-al-07, author = {D. Cremers and O. Fluck and M. Rousson and S. Aharon}, title = {A probabilistic level set formulation for interactive organ segmentation}, booktitle = {Proc. of the SPIE Medical Imaging}, year = {2007}, month = {feb}, address = {San Diego, USA}, editors = {E. Krupinski and A. Amini and M. Sonka}, keywords = {image segmentation, Parzen, Level Sets, medical imaging}, titleurl = {cremers_et_al_spie07.pdf}, topic = {Segmentation, Statistics, Level Sets}, } @inproceedings{Kohlberger-et-al-06, author = {T. Kohlberger and D. Cremers and M. Rousson and R. Ramaraj}, title = {4D shape priors for level set segmentation of the left myocardium in {SPECT} sequences}, booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)}, volume = {4190}, series = {LNCS}, pages = {92--100}, year = {2006}, month = {oct}, keywords = {image-segmentation,shape,Parzen,parametric,2d cardiac ultrasound,3d ct prostate, medical imaging}, titleurl = {kohlberger_et_al_miccai06.pdf}, topic = {Shape, Segmentation, Medical Image Analysis, Level Sets}, } @inproceedings{Schoenemann-et-al-11, author = {T. Schoenemann and S. Masnou and D. Cremers}, title = {On a linear programming approach to the discrete Willmore boundary value problem and generalizations}, booktitle = {Curves and Surfaces 2011}, year = {2011}, editor = {J.-D. Boissonnat et al.}, number = {6920}, series = {LNCS}, pages = {629--646}, titleurl = {schoenemann-et-al-11.pdf}, topic = {Shape Analysis}, } @article{DBLP:journals-pami-ManinisCCPLCG19, author = {K.-K. Maninis and S. Caelles and Y. Chen and J. PTand L. Leal-Taixé and D. Cremers and L. V Gool}, title = {Video Object Segmentation without Temporal Information}, journal = {{IEEE} Trans. Pattern Anal. Mach. Intell.}, volume = {41}, number = {6}, pages = {1515--1530}, year = {2019}, url = {https://doi.org/10.1109/TPAMI.2018.2838670}, doi = {10.1109/TPAMI.2018.2838670}, timestamp = {Sat, 30 May 2020 01:00:00 +0200}, biburl = {https://dblp.org/rec/journals/pami/ManinisCCPLCG19.bib}, bibsource = {dblp computer science bibliography, https://dblp.org}, } @article{GSC12:siims, author = {B. Goldluecke and E. Strekalovskiy and D. Cremers}, title = {The Natural Total Variation Which Arises from Geometric Measure Theory}, journal = {SIAM Journal on Imaging Sciences}, year = {2012}, volume = {5}, number = {2}, pages = {537–-563}, titleurl = {GSC12_siims.pdf}, topic = {Convex Relaxation Methods, Optic Flow}, keywords = {convex-relaxation, optical-flow}, } @inproceedings{GC09:SRF, author = {B. Goldluecke and D. Cremers}, title = {A Superresolution Framework for High-Accuracy Multiview Reconstruction}, booktitle = {Pattern Recognition (Proc. DAGM)}, year = {2009}, address = {Jena, Germany}, topic = {3D Reconstruction}, award = {Received DAGM Best Paper Award}, titleurl = {GC09_STD.pdf}, keywords = {3d-reconstruction}, } @inproceedings{GC09:SRT, author = {B. Goldluecke and D. Cremers}, title = {Superresolution Texture Maps for Multiview Reconstruction}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2009}, address = {Kyoto, Japan}, topic = {3D Reconstruction}, titleurl = {GC09_STM.pdf}, } @article{Sellent-et-al-pami11, author = {A. Sellent and M. Eisemann and B. Goldluecke and D. Cremers and M. Magnor}, title = {Motion Field Estimation from Alternate Exposure Images}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {33}, number = {8}, pages = {1577--1589}, year = {2011}, titleurl = {sellent_et_al_pami2011.pdf}, keywords = {optical-flow}, } @inproceedings{SEGPCM09:AEF, author = {A. Sellent and M. Eisemann and B. Goldluecke and T. Pock and D. Cremers and M. Magnor}, title = {{Variational Optical Flow from Alternate Exposure Images}}, booktitle = {Proceedings Vision, Modeling and Visualization (VMV)}, year = {2009}, pages = {135--143}, titleurl = {SEGPCM09_AEF.pdf}, keywords = {optical-flow}, } @inproceedings{Pock-et-al-iccv09, author = {T. Pock and D. Cremers and H. Bischof and A. Chambolle}, title = {An Algorithm for Minimizing the Piecewise Smooth Mumford-Shah Functional}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2009}, address = {Kyoto, Japan}, titleurl = {pock_et_al_iccv09.pdf}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation}, } @inproceedings{Wedel-et-al-iccv09, author = {A. Wedel and D. Cremers and T. Pock and H. Bischof}, title = {Structure- and Motion-adaptive Regularization for High Accuracy Optic Flow}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2009}, address = {Kyoto, Japan}, titleurl = {wedel_et_al_iccv09.pdf}, topic = {Motion}, keywords = {optical-flow}, } @inproceedings{Schoenemann-et-al-iccv09, author = {T. Schoenemann and F. Kahl and D. Cremers}, title = {Curvature Regularity for Region-based Image Segmentation and Inpainting: A Linear Programming Relaxation}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2009}, address = {Kyoto, Japan}, titleurl = {schoenemann_et_al_iccv09.pdf}, } @inproceedings{Windheuser-et-al-iccv09, author = {T. Windheuser and T. Schoenemann and D. Cremers}, title = {Beyond Connecting the Dots: A Polynomial-time Algorithm for Segmentation and Boundary Estimation with Imprecise User Input}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2009}, address = {Kyoto, Japan}, titleurl = {windheuser_et_al_iccv09.pdf}, } @inproceedings{Steinbruecker-et-al-iccv09, author = {F. Steinbruecker and T. Pock and D. Cremers}, title = {Large Displacement Optical Flow Computation without Warping}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2009}, address = {Kyoto, Japan}, titleurl = {steinbruecker_et_al_iccv09.pdf}, topic = {Motion}, keywords = {optical-flow}, } @inproceedings{Mitzel-et-al-dagm09, author = {D. Mitzel and T. Pock and T. Schoenemann and D. Cremers}, title = {Video Super Resolution using Duality Based TV-L1 Optical Flow}, booktitle = {Pattern Recognition (Proc. DAGM)}, year = {2009}, address = {Jena, Germany}, keywords = {optical-flow}, titleurl = {mitzel_et_al_dagm09.pdf}, } @inproceedings{GC10:VTV, title = {An Approach to Vectorial Total Variation based on Geometric Measure Theory}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, author = {B. Goldluecke and D. Cremers}, year = {2010}, titleurl = {GC10_VTV.pdf}, } @inproceedings{GC10:ML, title = {Convex Relaxation for Multilabel Problems with Product Label Spaces}, booktitle = {European Conference on Computer Vision (ECCV)}, author = {B. Goldluecke and D. Cremers}, year = {2010}, titleurl = {GC10_ML.pdf}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation}, } @inproceedings{souiai-et-al-wiccv13, author = {M. Souiai and C. Nieuwenhuis and E. Strekalovskiy and D. Cremers}, title = {Convex Optimization for Scene Understanding}, booktitle = {ICCV Workshop on Graphical Models for Scene Understanding}, year = {2013}, titleurl = {souiai-et-al-wiccv13.pdf}, keywords = {segmentation}, } @techreport{souiai_tr_13, author = {M. Souiai and E. Strekalovskiy and C. Nieuwenhuis and D. Cremers}, title = {Label Configuration Priors for Continuous Multi-Label Optimization}, type = {Technical report}, school = {Computer Vision Group, TU Munich}, titleurl = {souiai_tr_13.pdf}, year = {2013}, topic = {Convex Relaxation Methods,Image Segmentation}, keywords = {convex-relaxation,label configuration priors,medical imaging}, } @inproceedings{bergbauer-et-al-wiccv13, author = {J. Bergbauer and C. Nieuwenhuis and M. Souiai and D. Cremers}, title = {Proximity Priors for Variational Semantic Segmentation and Recognition}, booktitle = {ICCV Workshop on Graphical Models for Scene Understanding}, year = {2013}, titleurl = {bergbauer-et-al-wiccv13.pdf}, doi = {10.1109/ICCVW.2013.132}, keywords = {segmentation,diebold}, } @inproceedings{Strekalovskiy-Cremers-cvpr11, author = {E. Strekalovskiy and D. Cremers}, title = {Total Variation for Cyclic Structures: Convex Relaxation and Efficient Minimization}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2011}, address = {Colorado Springs, Colorado}, month = {jun}, titleurl = {strekalovskiy_cremers_cvpr11.pdf}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation}, } @inproceedings{GC11:iccv, author = {B. Goldluecke and D. Cremers}, title = {Introducing Total Curvature for Image Processing}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2011}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation}, titleurl = {GC11_iccv.pdf}, } @inproceedings{SGC11:iccv, author = {E. Strekalovskiy and B. Goldluecke and D. Cremers}, title = {Tight Convex Relaxations for Vector-Valued Labeling Problems}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2011}, titleurl = {sgc11_iccv.pdf}, topic = {Convex Relaxation Methods, Optic Flow}, keywords = {convex-relaxation,optical-flow}, } @inproceedings{AKGC11:iccv, author = {M. Aubry and K. Kolev and B. Goldluecke and D. Cremers}, title = {Decoupling Photometry and Geometry in Dense Variational Camera Calibration}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2011}, titleurl = {AKGC11_iccv.pdf}, keywords = {3d-reconstruction}, } @article{Kolev-et-al-PR11, author = {K. Kolev and N. Kirchgessner and S. Houben and A. Csiszar and W. Rubner and C. Palm and B. Eiben and R. Merkel and D. Cremers}, title = {A Variational Approach to Vesicle Membrane Reconstruction from Fluorescence Imaging}, journal = {Pattern Recognition}, volume = {44}, pages = {2944--2958}, year = {2011}, titleurl = {kolev_pr11.pdf}, keywords = {3d-reconstruction, medical imaging, biology}, } @article{Kolev_et_al_pami12, author = {K. Kolev and T. Brox and D. Cremers}, title = {Fast Joint Estimation of Silhouettes and Dense {3D} Geometry from Multiple Images}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2012}, volume = {34}, number = {3}, pages = {493--505}, titleurl = {Kolev_et_al_pami12.pdf}, topic = {3D Reconstruction}, keywords = {convex-relaxation, 3d-reconstruction}, } @inproceedings{Strekalovskiy-Cremers-iccv11, author = {E. Strekalovskiy and D. Cremers}, title = {Generalized Ordering Constraints for Multilabel Optimization}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2011}, titleurl = {strekalovskiy_cremers_iccv2011.pdf}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation}, } @inproceedings{sturm11rss-rgbd, title = {Towards a benchmark for RGB-D SLAM evaluation}, author = {J. Sturm and S. Magnenat and N. Engelhard and F. Pomerleau and F. Colas and W. Burgard and D. Cremers and R. Siegwart}, booktitle = {Proc. of the RGB-D Workshop on Advanced Reasoning with Depth Cameras at Robotics: Science and Systems Conf.~(RSS)}, address = {Los Angeles, USA}, year = {2011}, month = {June}, keywords = {rgb-d,rgb-d benchmark,vslam}, titleurl = {sturm_et_al_rss11-talk.pdf}, } @inproceedings{Nieuwenhuis_emmcvpr11, author = {C. Nieuwenhuis and E. Toeppe and D. Cremers}, title = {Space-Varying Color Distributions for Interactive Multiregion Segmentation: Discrete versus Continuous Approaches}, year = {2011}, booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, topic = {segmentation, Convex Relaxation Methods}, titleurl = {nieuwenhuis_et_al_emmcvpr11.pdf}, pages = {177-190}, keywords = {segmentation, convex-relaxation}, } @inproceedings{KC11:iccv, author = {M. Klodt and D. Cremers}, title = {A Convex Framework for Image Segmentation with Moment Constraints}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2011}, titleurl = {kc11_iccv.pdf}, topic = {segmentation, Convex Relaxation Methods}, keywords = {segmentation, convex-relaxation, medical imaging}, } @inproceedings{aubry-et-al-4dmod11, author = {M. Aubry and U. Schlickewei and D. Cremers}, title = {The Wave Kernel Signature: A Quantum Mechanical Approach To Shape Analysis}, booktitle = {I{EEE} {I}nternational {C}onference on {C}omputer {V}ision ({ICCV}) - {W}orkshop on {D}ynamic {S}hape {C}apture and {A}nalysis ({4DMOD})}, year = {2011}, titleurl = {compute_WKS.m}, keywords = {Shape Analysis}, topic = {Shape Analysis}, } @inproceedings{steinbruecker_sturm_cremers_iccv11, author = {F. Steinbruecker and J. Sturm and D. Cremers}, title = {Real-Time Visual Odometry from Dense RGB-D Images}, booktitle = {Workshop on Live Dense Reconstruction with Moving Cameras at the Intl. Conf. on Computer Vision (ICCV)}, year = {2011}, keywords = {dense visual odometry,rgb-d,rgb-d benchmark,vo,vslam}, } @inproceedings{Steinbruecker-et-al-vmv09, author = {F. Steinbruecker and T. Pock and D. Cremers}, title = {Advanced Data Terms for Variational Optic Flow Estimation}, booktitle = {Proceedings Vision, Modeling and Visualization (VMV)}, year = {2009}, address = {Braunschweig, Germany}, titleurl = {steinbruecker_et_al_vmv09.pdf}, topic = {Motion}, keywords = {optical-flow}, } @inproceedings{endres12icra, author = {F. Endres and J. Hess and N. Engelhard and J. Sturm and D. Cremers and W. Burgard}, title = {An Evaluation of the {RGB-D SLAM} System}, booktitle = {International Conference on Robotics and Automation (ICRA)}, address = {St. Paul, MA, USA}, month = {May}, year = {2012}, keywords = {rgb-d,rgb-d benchmark,sturmselection,vslam}, } @inproceedings{ruehr12icra, author = {T. Ruehr and J. Sturm and D. Pangercic and M. Beetz and D. Cremers}, title = {A Generalized Framework for Opening Doors and Drawers in Kitchen Environments}, booktitle = {International Conference on Robotics and Automation (ICRA)}, address = {St. Paul, MA, USA}, month = {May}, year = {2012}, } @inproceedings{SOKC-11, author = {M. Schikora and M.Oispuu and W. Koch and D. Cremers}, title = { Multiple Source Localization Based on Biased Bearings Using the Intensity Filter - Approach and Experimental Results }, month = {December}, year = {2011}, booktitle = { 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing}, address = { San Juan, Puerto Rico,}, titleurl = {2011_SOKC_CAMSAP.pdf}, } @inproceedings{MSKC-11, author = {S. Madhogaria and M. Schikora and W. Koch and D. Cremers}, title = {Pixel-based Classification Method for Detecting Unhealthy Regions in Leaf Images}, month = {September}, year = {2011}, booktitle = {6th IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF) }, address = {Berlin, Germany}, titleurl = {2011_MSKC_SDF.pdf}, keywords = {biology}, } @inproceedings{SKSC-11, author = {M. Schikora and W. Koch and R.L. Streit and D. Cremers}, title = {Sequential Monte Carlo Method for the iFilter}, month = {July}, year = {2011}, booktitle = {14th International Conference on Information Fusion (FUSION)}, address = {Chicago, IL, USA}, titleurl = {2011_SKSC_fusion.pdf}, } @inproceedings{SKC-11, author = {M. Schikora and W. Koch and D. Cremers}, title = {Multi-object tracking via high accuracy optical flow and finite set statistics}, month = {Mai}, year = {2011}, booktitle = {International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Prag, Czech Republic}, keywords = {optical-flow}, titleurl = {2011_SKC_ICASSP.pdf}, } @incollection{SKSC-book-2012, author = {M. Schikora and W. Koch and R. L. Streit and D. Cremers}, title = { A Sequential Monte Carlo Method for Multi-Target Tracking with the Intensity Filter}, booktitle = { Advances in Intelligent Signal Processing and Data Mining}, publisher = {Springer-Verlag Berlin Heidelberg}, pages = {55-87}, year = {2012}, titleurl = {2012_SKSC_book.pdf}, } @inproceedings{Toeppe-et-al-LNCS-2011, author = {E. Toeppe and M. R. Oswald and D. Cremers and C. Rother}, title = {Silhouette-Based Variational Methods for Single View Reconstruction}, booktitle = {Proceedings of the 2010 international conference on Video Processing and Computational Video}, editor = {D. Cremers and M. A. Magnor and M. R. Oswald and L. Zelnik-Manor}, year = {2011}, isbn = {978-3-642-24869-6}, location = {Dagstuhl Castle, Germany}, pages = {104--123}, numpages = {20}, publisher = {Springer-Verlag}, address = {Berlin, Heidelberg}, keywords = {convex-relaxation, convex optimization, image-based modeling, singleview}, } @inproceedings{2012_SGMCKS_DFTT, address = {London, UK}, author = {M. Schikora and A. Gning and L. Mihaylova and D. Cremers and W. Koch and R. Streit}, booktitle = {9th IET Data Fusion and Target Tracking Conference}, month = {May}, title = {Box-Particle Intensity Filter}, year = {2012}, titleurl = {2012_SGMCKS_DFTT.pdf}, } @inproceedings{2012_SGMCK_FUSION, address = {Singapore}, author = {M. Schikora and A. Gning and L. Mihaylova and D. Cremers and W. Koch}, booktitle = {15th International Conference on Information Fusion (FUSION)}, month = {July}, title = {Box-Particle PHD Filter for Multi-Target Tracking}, year = {2012}, titleurl = {2012_SGMCK_FUSION.pdf}, } @article{2012_SNMKCHKS_BIOINF, author = {M. Schikora and B. Neupane and S. Madhogaria and W. Koch and D. Cremers and H. Hirt and K.-H. Kogel and A. Schikora}, journal = {BMC Bioinformatics}, month = {July}, number = {171}, title = {An image classification approach to analyze the suppression of plant immunity by the human pathogen Salmonella Typhimurium}, volume = {13}, year = {2012}, titleurl = {2012_SNMKCHKS_BIOINF.pdf}, keywords = {biology}, } @article{Madhogaria-et-al-taes15, author = {S. Madhogaria and P. M. Baggenstoss and M. Schikora and W. Koch and D. Cremers}, journal = {IEEE T. on Aerospace and Electronic Systems}, number = {1}, title = {Car detection by fusion of HOG and causal MRF}, volume = {51}, pages = {575--590}, year = {2015}, } @inproceedings{HenschelLCR18, author = {R. Henschel and L. Leal-Taixé and D. Cremers and B. Rosenhahn}, title = {Fusion of Head and Full-Body Detectors for Multi-Object Tracking}, booktitle = {2018 {IEEE} Conference on Computer Vision and Pattern Recognition Workshops, {CVPR} Workshops 2018, Salt Lake City, UT, USA, June 18-22, 2018}, pages = {1428--1437}, publisher = {{IEEE} Computer Society}, year = {2018}, } @article{Klodt-et-al-bmc15, author = {M. Klodt and K. Herzog and R. Töpfer and D. Cremers}, journal = {BMC Bioinformatics}, month = {May}, number = {143}, title = {Field phenotyping of grapevine growth using dense stereo reconstruction}, volume = {16}, year = {2015}, keywords = {biology}, } @inproceedings{zhang12iros, author = {L. Zhang and J. Sturm and D. Cremers and D. Lee}, title = {Real-Time Human Motion Tracking using Multiple Depth Cameras}, booktitle = {Proc. of the International Conference on Intelligent Robot Systems (IROS)}, year = {2012}, month = {Oct.}, keywords = {rgb-d}, } @inproceedings{Strekalovskiy-et-al-eccv12, author = {E. Strekalovskiy and C. Nieuwenhuis and D. Cremers}, title = {Nonmetric Priors for Continuous Multilabel Optimization}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2012}, address = {Firenze, Italy}, month = {oct}, publisher = {Springer}, topic = {Convex Relaxation Methods,Segmentation}, keywords = {convex-relaxation, Segmentation}, } @inproceedings{Windheuser-et-al-eccv12, author = {T. Windheuser and H. Ishikawa and D. Cremers}, title = {Generalized Roof Duality for Multi-Label Optimization: Optimal Lower Bounds and Persistency}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2012}, address = {Firenze, Italy}, month = {oct}, } @inproceedings{Windheuser-et-al-miru12, author = {T. Windheuser and H. Ishikawa and D. Cremers}, title = {QPBOアルゴリズムの多値化による非劣モジュラエネルギー最小化 [QPBO arugorizumu no tachika ni yoru hiretsu mojura enerugī saishōka]}, booktitle = {Meeting on Image Recognition and Understanding}, year = {2012}, address = {Fukuoka, Japan}, month = {aug}, } @inproceedings{Oswald-et-al-cvpr12, author = {M. R. Oswald and E. Toeppe and D. Cremers}, title = {Fast and Globally Optimal Single View Reconstruction of Curved Objects}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2012}, address = {Providence, Rhode Island}, month = {jun}, titleurl = {oswald_toeppe_cremers_cvpr12.pdf}, topic = {Single View Reconstruction}, pages = {534-541}, keywords = {convex-relaxation, singleview}, } @inproceedings{Strekalovskiy-et-al-cvpr12, author = {E. Strekalovskiy and A. Chambolle and D. Cremers}, title = {A Convex Representation for the Vectorial Mumford-Shah Functional}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2012}, address = {Providence, Rhode Island}, month = {jun}, titleurl = {strekalovskiy_chambolle_cremers_cvpr12.pdf}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation}, } @inproceedings{engel12iros, author = {J. Engel and J. Sturm and D. Cremers}, title = {Camera-Based Navigation of a Low-Cost Quadrocopter}, booktitle = {Proc. of the International Conference on Intelligent Robot Systems (IROS)}, year = {2012}, month = {Oct.}, keywords = {quadrocopter,ardrone,sturmselection,vslam}, } @inproceedings{sturm12iros, author = {J. Sturm and N. Engelhard and F. Endres and W. Burgard and D. Cremers}, title = {A Benchmark for the Evaluation of RGB-D SLAM Systems}, booktitle = {Proc. of the International Conference on Intelligent Robot Systems (IROS)}, year = {2012}, month = {Oct.}, keywords = {rgb-d,rgb-d benchmark,sturmselection,dataset,vslam}, } @inproceedings{engel12vicomor, author = {J. Engel and J. Sturm and D. Cremers}, title = {Accurate Figure Flying with a Quadrocopter Using Onboard Visual and Inertial Sensing}, booktitle = {Proc. of the Workshop on Visual Control of Mobile Robots (ViCoMoR) at the IEEE/RJS International Conference on Intelligent Robot Systems (IROS)}, year = {2012}, month = {Oct.}, keywords = {quadrocopter, ardrone,inertial,vslam}, } @article{nieuwenhuis-cremers-pami12_2, author = {C. Nieuwenhuis and D. Cremers}, title = {Spatially Varying Color Distributions for Interactive Multi-Label Segmentation}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2013}, volume = {35}, number = {5}, pages = {1234-1247}, titleurl = {nieuwenhuis-cremers-pami12_2.pdf}, topic = {Segmentation}, keywords = {convex-relaxation, segmentation, medical imaging}, } @inproceedings{sturm12iros_ws, author = {J. Sturm and W. Burgard and D. Cremers}, title = {Evaluating Egomotion and Structure-from-Motion Approaches Using the {TUM RGB-D} Benchmark}, booktitle = {Proc. of the Workshop on Color-Depth Camera Fusion in Robotics at the IEEE/RJS International Conference on Intelligent Robot Systems (IROS)}, year = {2012}, month = {Oct.}, keywords = {rgb-d,rgb-d benchmark,dataset,vo,vslam}, } @inproceedings{Golkov-et-al-ismrm14-6d-cs, author = {V. Golkov and M.I. Menzel and T. Sprenger and M. Souiai and A. Haase and D. Cremers and J.I. Sperl}, title = {Direct Reconstruction of the Average Diffusion Propagator with Simultaneous Compressed-Sensing-Accelerated Diffusion Spectrum Imaging and Image Denoising by Means of Total Generalized Variation Regularization}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, year = {2014}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing, total generalized variation, primal-dual}, } @inproceedings{Golkov-et-al-ismrm14-semi-joint, author = {V. Golkov and M.I. Menzel and T. Sprenger and A. Haase and D. Cremers and J.I. Sperl}, title = {Semi-Joint Reconstruction for Diffusion {MRI} Denoising Imposing Similarity of Edges in Similar Diffusion-Weighted Images}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, year = {2014}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, } @inproceedings{sommer18joint, author = {C. Sommer and D. Cremers}, title = {Joint Representation of Primitive and Non-primitive Objects for 3D Vision}, booktitle = {2018 International Conference on 3D Vision, 3DV 2018, Verona, Italy, September 5-8, 2018}, pages = {160--169}, publisher = {{IEEE} Computer Society}, doi = {10.1109/3DV.2018.00028}, year = {2018}, keywords = {Geometry Processing, SLAM}, } @inproceedings{Golkov-et-al-ohbm14, author = {V. Golkov and M.I. Menzel and T. Sprenger and M. Souiai and A. Haase and D. Cremers and J.I. Sperl}, title = {Improved Diffusion Kurtosis Imaging and Direct Propagator Estimation Using {6-D} Compressed Sensing}, booktitle = {Organization for Human Brain Mapping (OHBM) Annual Meeting}, year = {2014}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing, total generalized variation, primal-dual}, } @incollection{Golkov-et-al-cdmri14, author = {V. Golkov and J.I. Sperl and M.I. Menzel and T. Sprenger and E.T. Tan and L. Marinelli and C.J. Hardy and A. Haase and D. Cremers}, title = {Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per {q}-Space Coordinate}, booktitle = {Computational Diffusion {MRI}}, publisher = {Springer}, year = {2014}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, total generalized variation, super-resolution, primal-dual}, award = {Book Chapter, and Oral Presentation at {MICCAI} 2014 Workshop on Computational Diffusion {MRI}}, } @inproceedings{Golkov-et-al-esmrmb13-comparison, author = {V. Golkov and T. Sprenger and A. Menini and M.I. Menzel and D. Cremers and J.I. Sperl}, title = {Effects of Low-Rank Constraints, Line-Process Denoising, and {q}-Space Compressed Sensing on Diffusion {MR} Image Reconstruction and Kurtosis Tensor Estimation}, booktitle = {European Society for Magnetic Resonance in Medicine and Biology ({ESMRMB}) Annual Meeting}, year = {2013}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, award = {Oral Presentation}, } @inproceedings{Golkov-et-al-esmrmb13-iic-nnc, author = {V. Golkov and T. Sprenger and M.I. Menzel and D. Cremers and J.I. Sperl}, title = {Line-Process-Based Joint {SENSE} Reconstruction of Diffusion Images with Intensity Inhomogeneity Correction and Noise Non-Stationarity Correction}, booktitle = {European Society for Magnetic Resonance in Medicine and Biology ({ESMRMB}) Annual Meeting}, year = {2013}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging}, award = {Certificate of Merit Award}, } @inproceedings{Golkov-et-al-dsismrm13, author = {V. Golkov and M.I. Menzel and T. Sprenger and A. Menini and D. Cremers and J.I. Sperl}, title = {Reconstruction, Regularization, and Quality in Diffusion {MRI} Using the Example of Accelerated Diffusion Spectrum Imaging}, booktitle = {16th Annual Meeting of the German Chapter of the {ISMRM}}, year = {2013}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, award = {Oral Presentation}, } @inproceedings{Golkov-et-al-podstrana13, author = {V. Golkov and M.I. Menzel and T. Sprenger and A. Menini and D. Cremers and J.I. Sperl}, title = {Corrected Joint {SENSE} Reconstruction, Low-Rank Constraints, and Compressed-Sensing-Accelerated Diffusion Spectrum Imaging in Denoising and Kurtosis Tensor Estimation}, booktitle = {{ISMRM} Workshop on Diffusion as a Probe of Neural Tissue Microstructure}, year = {2013}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, } @inproceedings{Golkov-et-al-ismrm13, author = {V. Golkov and T. Sprenger and M.I. Menzel and E.T. Tan and K.F. King and C.J. Hardy and L. Marinelli and D. Cremers and J.I. Sperl}, title = {Noise Reduction in Accelerated Diffusion Spectrum Imaging through Integration of {SENSE} Reconstruction into Joint Reconstruction in Combination with {q}-Space Compressed Sensing}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, year = {2013}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, } @article{nieuwenhuis-et-al-ijcv13, author = {C. Nieuwenhuis and E. Toeppe and D. Cremers}, title = {A Survey and Comparison of Discrete and Continuous Multi-label Optimization Approaches for the Potts Model}, journal = {International Journal of Computer Vision}, volume = {104}, number = {3}, pages = {223-240}, year = {2013}, month = {sep}, keywords = {convex-relaxation, Segmentation}, } @inproceedings{Oswald-et-al-LNCS-2012, author = {M. R. Oswald and E. Toeppe and C. Nieuwenhuis and D. Cremers}, title = {A Survey on Geometry Recovery from a Single Image with Focus on Curved Object Reconstruction}, booktitle = {Proceedings of the 2011 Conference on Innovations for Shape Analysis: Models and Algorithms}, year = {2011}, location = {Dagstuhl Castle, Germany}, publisher = {Springer-Verlag}, keywords = {singleview}, titleurl = {Oswald-et-al_2011-survey.pdf}, } @article{Cremers_Strekalovskiy_jmiv12, author = {D. Cremers and E. Strekalovskiy}, title = {Total Cyclic Variation and Generalizations}, journal = {Journal of Mathematical Imaging and Vision}, year = {2012}, month = {nov}, volume = {47}, number = {3}, pages = {258--277}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation}, titleurl = {Cremers_Strekalovskiy_jmiv12.pdf}, } @inproceedings{ufer_et_al_eccv12, author = {N. Ufer and M. Souiai and D. Cremers}, title = {Wehrli 2.0: An Algorithm for ”Tidying up Art”}, booktitle = {VISART “Where Computer Vision Meets Art” workshop, ECCV 2012}, year = {2012}, address = {Firenze, Italy}, month = {oct}, publisher = {Springer}, topic = {Segmentation}, keywords = {convex-relaxation, segmentation}, titleurl = {ufer_et_al_eccv12.pdf}, } @inproceedings{kerl13icra, title = {Robust Odometry Estimation for RGB-D Cameras}, author = {C. Kerl and J. Sturm and D. Cremers}, booktitle = {International Conference on Robotics and Automation (ICRA)}, year = {2013}, month = {May}, award = {Best Vision Paper Award - Finalist}, keywords = {dense visual odometry,rgb-d,rgb-d benchmark,sturmselection,vo,vslam}, } @inproceedings{toeppe_et_al_cvpr13, author = {E. Toeppe and C. Nieuwenhuis and D. Cremers}, title = {Volume Constraints for Single View Reconstruction}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2013}, address = {Portland, USA}, topic = {Segmentation}, keywords = {singleview, convex-relaxation,segmentation}, titleurl = {toeppe_et_al_cvpr13.pdf}, } @article{GSC13:vml, author = {B. Goldluecke and E. Strekalovskiy and D. Cremers}, title = {Tight Convex Relaxations for Vector-Valued Labeling}, journal = {SIAM Journal on Imaging Sciences}, year = {2013}, volume = {6}, number = {3}, pages = {1626–-1664}, titleurl = {GSC13_siims.pdf}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation}, } @inproceedings{bylow_etal_rss2013, title = {Real-Time Camera Tracking and 3D Reconstruction Using Signed Distance Functions}, author = {E. Bylow and J. Sturm and C. Kerl and F. Kahl and D. Cremers}, booktitle = {Robotics: Science and Systems Conference (RSS)}, year = {2013}, month = {June}, keywords = {rgb-d,rgb-d benchmark,sturmselection,vslam}, } @inproceedings{bylow_etal_rss2013rgbd_workshop, title = {Direct Camera Pose Tracking and Mapping With Signed Distance Functions}, author = {E. Bylow and J. Sturm and C. Kerl and F. Kahl and D. Cremers}, booktitle = {Demo Track of the RGB-D Workshop on Advanced Reasoning with Depth Cameras at the Robotics: Science and Systems Conference (RSS)}, year = {2013}, month = {June}, keywords = {rgb-d,rgb-d benchmark,vslam}, } @inproceedings{souiai-et-al-emmcvpr13, author = {M. Souiai and E. Strekalovskiy and C. Nieuwenhuis and D. Cremers}, title = {A Co-occurrence Prior for Continuous Multi-Label Optimization}, booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, year = {2013}, topic = {Convex Relaxation Methods, Segmentation}, keywords = {convex-relaxation}, } @inproceedings{stangl-et-al-13, author = {F. Stangl and M. Souiai and D. Cremers}, title = {Performance Evaluation of Narrow Band Methods for Variational Stereo}, booktitle = {35th German Conference on Pattern Recognition (GCPR)}, year = {2013}, titleurl = {stangl_et_al_gcpr13.pdf}, keywords = {convex-relaxation}, } @inproceedings{moellenhoff-et-al-13, author = {T. Möllenhoff and C. Nieuwenhuis and E. Toeppe and D. Cremers}, title = {Efficient Convex Optimization for Minimal Partition Problems with Volume Constraints}, booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, year = {2013}, titleurl = {moellenhoff_et_al_13.pdf}, keywords = {convex-relaxation}, } @inproceedings{kerl13iros, author = {C. Kerl and J. Sturm and D. Cremers}, title = {Dense Visual SLAM for RGB-D Cameras}, booktitle = {Proc. of the Int. Conf. on Intelligent Robot Systems (IROS)}, year = {2013}, keywords = {dense visual odometry,dense visual slam,rgb-d,rgb-d benchmark,vslam}, } @inproceedings{naseer2013iros, author = {T. Naseer and J. Sturm and D. Cremers}, title = {{FollowMe}: Person Following and Gesture Recognition with a Quadrocopter}, booktitle = {Proc. of the Int. Conf. on Intelligent Robot Systems (IROS)}, year = {2013}, keywords = {rgb-d,quadrocopter}, } @inproceedings{klodt_etal_2013gcpr, author = {M. Klodt and J. Sturm and D. Cremers}, title = {Scale-Aware Object Tracking with Convex Shape Constraints on RGB-D Images}, booktitle = {German Conference on Pattern Recognition (GCPR)}, year = {2013}, address = {Saarbr\"ucken, Germany}, month = {September}, keywords = {convex-relaxation, rgb-d}, titleurl = {klodt_etal_2013gcpr.pdf}, } @article{endres2013tro, author = {F. Endres and J. Hess and J. Sturm and D. Cremers and W. Burgard}, title = {3D Mapping with an {RGB-D} Camera}, journal = {IEEE Transactions on Robotics (T-RO)}, volume = {30}, number = {1}, pages = {177-187}, year = {2013}, keywords = {vslam}, } @inproceedings{sturm_etal_2013uavg, author = {J. Sturm and E. Bylow and F. Kahl and D. Cremers}, title = {Dense Tracking and Mapping with a Quadrocopter}, year = {2013}, address = {Rostock, Germany}, month = {September}, booktitle = {Unmanned Aerial Vehicle in Geomatics (UAV-g)}, keywords = {rgb-d,rgb-d benchmark,quadrocopter,vslam}, } @inproceedings{bender_etal_2013uavg, author = {D. Bender and M. Schikora and J. Sturm and D. Cremers}, title = {Graph-based bundle adjustment for INS-camera calibration}, year = {2013}, address = {Rostock, Germany}, month = {September}, award = {Best research paper award}, booktitle = {Unmanned Aerial Vehicle in Geomatics (UAV-g)}, keywords = {quadrocopter,sturmselection}, } @inproceedings{sturm_etal_2013gcpr, author = {J. Sturm and E. Bylow and F. Kahl and D. Cremers}, title = {{CopyMe3D}: Scanning and Printing Persons in {3D}}, booktitle = {German Conference on Pattern Recognition (GCPR)}, year = {2013}, address = {Saarbr\"ucken, Germany}, month = {September}, keywords = {rgb-d,rgb-d benchmark,sturmselection}, } @article{liu2013introduction, title = {Introduction to the special issue on visual understanding and applications with RGB-D cameras}, author = {Z. Liu and M. Beetz and D. Cremers and J. Gall and W. Li and D. Pangercic and J. Sturm and Y.-W. Tai}, journal = {Journal of Visual Communication and Image Representation (JVCI)}, year = {2013}, } @inproceedings{rodola-bmvc13, author = {E. Rodola and T. Harada and Y. Kuniyoshi and D. Cremers}, title = {Efficient Shape Matching using Vector Extrapolation}, booktitle = {British Machine Vision Conference (BMVC)}, year = {2013}, titleurl = {rodola-bmvc13.pdf}, topic = {Shape Analysis, Discrete Optimization}, } @inproceedings{engel2013iccv, author = {J. Engel and J. Sturm and D. Cremers}, title = {Semi-Dense Visual Odometry for a Monocular Camera}, year = {2013}, address = {Sydney, Australia}, month = {December}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, keywords = {rgb-d, visual odometry, monocular, slam, semidense, vslam}, } @inproceedings{rodola-iccv13, author = {E. Rodola and A. Torsello and T. Harada and Y. Kuniyoshi and D. Cremers}, title = {Elastic Net Constraints for Shape Matching}, year = {2013}, address = {Sydney, Australia}, month = {December}, titleurl = {enet_project.zip}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, topic = {Shape Matching, Shape Analysis}, } @inproceedings{lellmann-et-al-iccv2013, author = {J. Lellmann and E. Strekalovskiy and S. Koetter and D. Cremers}, title = {Total Variation Regularization for Functions with Values in a Manifold}, year = {2013}, address = {Sydney, Australia}, month = {December}, titleurl = {lellmann-et-al-iccv2013.pdf}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation, Segmentation}, } @inproceedings{Nieuwenhuis-et-al-iccv13, author = {C. Nieuwenhuis and E. Strekalovskiy and D. Cremers}, title = {Proportion Priors for Image Sequence Segmentation}, year = {2013}, address = {Sydney, Australia}, month = {December}, titleurl = {nieuwenhuis-et-al-iccv2013.pdf}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation, Segmentation}, } @inproceedings{stuehmer-et-al-iccv2013, author = {J. Stühmer and P. Schröder and D. Cremers}, title = {Tree Shape Priors with Connectivity Constraints using Convex Relaxation on General Graphs}, year = {2013}, address = {Sydney, Australia}, month = {December}, titleurl = {stuehmer-et-al-iccv2013.pdf}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, topic = {Segmentation, Shape Priors}, keywords = {Convex-Relaxation, Segmentation, shape-priors, medical imaging}, award = {Oral Presentation}, } @inproceedings{KuschkBC17, author = {G. Kuschk and A. Bozic and D. Cremers}, title = {Real-time variational stereo reconstruction with applications to large-scale dense {SLAM}}, booktitle = {{IEEE} Intelligent Vehicles Symposium, {IV} 2017, Los Angeles, CA, USA, June 11-14, 2017}, pages = {1348--1355}, publisher = {{IEEE}}, year = {2017}, keywords = {vslam}, } @inproceedings{kuschk-et-al-iccv2013, author = {G. Kuschk and D. Cremers}, title = {Fast and Accurate Large-scale Stereo Reconstruction using Variational Methods}, year = {2013}, address = {Sydney, Australia}, month = {December}, titleurl = {kuschk-et-al-iccv2013.pdf}, booktitle = {ICCV Workshop on Big Data in 3D Computer Vision}, keywords = {large scale, 3d-reconstruction, total variation}, } @article{DuranMSC16, author = {J. Duran and M. Möller and C. Sbert and D. Cremers}, title = {Collaborative Total Variation: {A} General Framework for Vectorial {TV} Models}, titleurl = {Duran_et_al_siims2016.pdf}, journal = {{SIAM} J. Imaging Sci.}, volume = {9}, number = {1}, pages = {116--151}, year = {2016}, } @article{BurgerGMEC16, author = {M. Burger and G. Gilboa and M. Möller and L. Eckardt and D. Cremers}, title = {Spectral Decompositions Using One-Homogeneous Functionals}, journal = {{SIAM} J. Imaging Sci.}, volume = {9}, number = {3}, pages = {1374--1408}, year = {2016}, } @inproceedings{Oswald-Cremers-ICCV-4DMOD-2013, author = {M. R. Oswald and D. Cremers}, title = {A Convex Relaxation Approach to Space Time Multi-view 3D Reconstruction}, booktitle = {{ICCV} {W}orkshop on {D}ynamic {S}hape {C}apture and {A}nalysis ({4DMOD})}, year = {2013}, numpages = {8}, titleurl = {oswald-cremers-ICCV-4DMOD2013.pdf}, topic = {3D Reconstruction}, keywords = {space-time, 3d-reconstruction, convex-relaxation}, } @inproceedings{Steinbruecker-etal-iccv13, author = {F. Steinbruecker and C. Kerl and J. Sturm and D. Cremers}, title = {Large-Scale Multi-Resolution Surface Reconstruction from RGB-D Sequences}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2013}, address = {Sydney, Australia}, titleurl = {steinbruecker_etal_iccv2013.pdf}, topic = {3D Reconstruction}, keywords = {RGB-D,Fusion,3d-reconstruction,vslam}, } @article{strekalovskiy-et-al-siims14, author = {E. Strekalovskiy and A. Chambolle and D. Cremers}, title = {Convex Relaxation of Vectorial Problems with Coupled Regularization}, journal = {SIAM Journal on Imaging Sciences}, year = {2014}, volume = {7}, number = {1}, pages = {294--336}, topic = {Convex Relaxation Methods, Optic Flow}, keywords = {convex-relaxation, optical-flow}, } @inproceedings{naseer13iros_ws, author = {T. Naseer and J. Sturm and D. Cremers}, title = {Interactive Person Following and Gesture Recognition with a Flying Robot}, booktitle = {Proc. of the Assistance and Service Robotics Workshop (ASROB) at the IEEE.~Int.~Conf.~on Intelligent Robots and Systems (IROS)}, year = {2013}, month = {Nov.}, } @inproceedings{Steinbruecker-etal-icra14, author = {F. Steinbruecker and J. Sturm and D. Cremers}, title = {Volumetric 3D Mapping in Real-Time on a CPU}, booktitle = {International Conference on Robotics and Automation (ICRA)}, year = {2014}, address = {Hongkong, China}, titleurl = {steinbruecker_etal_icra2014.pdf}, topic = {3D Reconstruction}, keywords = {RGB-D,Fusion,3d-reconstruction,vslam}, } @article{engel14ras, author = {J. Engel and J. Sturm and D. Cremers}, title = {Scale-Aware Navigation of a Low-Cost Quadrocopter with a Monocular Camera}, journal = {Robotics and Autonomous Systems (RAS)}, year = {2014}, volume = {62}, number = {11}, pages = {1646-–1656}, topic = {quadrocopter, ardrone}, keywords = {quadrocopter, ardrone, vslam}, } @inproceedings{rodola-cvpr14, author = {E. Rodola and S. R Bulo and T. Windheuser and M. Vestner and D. Cremers}, title = {Dense Non-Rigid Shape Correspondence Using Random Forests}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2014}, titleurl = {randomforests.zip}, topic = {Shape Analysis, Shape Matching, Machine Learning}, } @inproceedings{kee-cvpr14, author = {Y. Kee and M. Souiai and D. Cremers and J. Kim}, title = {Sequential Convex Relaxation for Mutual-Information-Based Unsupervised Figure-Ground Segmentation}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2014}, titleurl = {kee-cvpr14.pdf}, topic = {Image Segmentation, Convex Relaxation, Information Theory}, } @article{KeeLSCK17, author = {Y. Kee and Y. Lee and M. Souiai and D. Cremers and J. Kim}, title = {Sequential Convex Programming for Computing Information-Theoretic Minimal Partitions: Nonconvex Nonsmooth Optimization}, journal = {{SIAM} J. Imaging Sci.}, volume = {10}, number = {4}, pages = {1845--1877}, year = {2017}, } @inproceedings{alvarez14iser, author = {H. Alvarez and L.M. Paz and J. Sturm and D. Cremers}, title = {Collision Avoidance for Quadrotors with a Monocular Camera}, booktitle = {Proc. of The 12th International Symposium on Experimental Robotics (ISER)}, year = {2014}, keywords = {vslam}, } @article{rodola-cgf14, author = {E. Rodola and S. R Bulo and D. Cremers}, title = {Robust Region Detection via Consensus Segmentation of Deformable Shapes}, journal = {Computer Graphics Forum}, volume = {33}, number = {5}, year = {2014}, pages = {97--106}, publisher = {Wiley}, topic = {Shape Analysis, Machine Learning, Segmentation}, titleurl = {consensus_demo.zip}, } @article{Cremers17, author = {D Cremers}, title = {Computer Vision f{\"{u}}r 3-D-Rekonstruktion - Vom Nischenthema zum Mainstream}, journal = {Informatik Spektrum}, volume = {40}, number = {2}, pages = {205--209}, year = {2017}, } @inproceedings{engel14eccv, author = {J. Engel and T. Schöps and D. Cremers}, title = {{LSD-SLAM}: Large-Scale Direct Monocular {SLAM}}, year = {2014}, month = {September}, booktitle = {European Conference on Computer Vision (ECCV)}, keywords = {rgb-d, monocular, slam, semidense, lsdslam, vslam}, award = {Oral Presentation}, } @inproceedings{schoeps14ismar, author = {T. Schöps and J. Engel and D. Cremers}, title = {Semi-Dense Visual Odometry for {AR} on a Smartphone}, year = {2014}, month = {September}, booktitle = {International Symposium on Mixed and Augmented Reality}, keywords = {rgb-d, monocular, slam, semidense, lsdslam, ar, vo, vslam}, award = {Best Short Paper Award}, } @inproceedings{cremers-dcurv14, author = {D. Cremers and E. Rodola and T. Windheuser}, title = {Relaxations for Minimizing Metric Distortion and Elastic Energies for 3D Shape Matching}, booktitle = {Actes des recontres du CIRM: Courbure discrete: théorie et applications}, year = {2013}, volume = {3}, number = {1}, pages = {107--117}, topic = {Shape Analysis, Shape Matching}, } @inproceedings{windheus-bmvc14, author = {T. Windheuser and M. Vestner and E. Rodola and R. Triebel and D. Cremers}, title = {Optimal Intrinsic Descriptors for Non-Rigid Shape Analysis}, booktitle = {British Machine Vision Conference (BMVC)}, year = {2014}, titleurl = {windheus-bmvc14.pdf}, topic = {Shape Analysis, Shape Matching, Machine Learning}, } @inproceedings{Strobel-et-al-gcpr2014, author = {M. Strobel and J. Diebold and D. Cremers}, title = {Flow and Color Inpainting for Video Completion}, booktitle = {German Conference on Pattern Recognition (GCPR)}, year = {2014}, address = {M\"unster, Germany}, month = {September}, keywords = {video completion, video inpainting, disocclusion, temporal consistency, segmentation, optical flow,diebold}, doi = {10.1007/978-3-319-11752-2_23}, award = {Oral Presentation}, } @inproceedings{maier2014gcpr, author = {R. Maier and J. Sturm and D. Cremers}, title = {Submap-based Bundle Adjustment for 3D Reconstruction from RGB-D Data}, booktitle = {German Conference on Pattern Recognition (GCPR)}, year = {2014}, address = {M\"unster, Germany}, month = {September}, keywords = {rgb-d,rgb-d benchmark,vslam}, award = {Oral Presentation}, } @inproceedings{Gurdan-et-al-GCPR-2014, author = {T. Gurdan and M. R. Oswald and D. Gurdan and D. Cremers}, title = {Spatial and Temporal Interpolation of Multi-View Image Sequences}, month = {sep}, year = {2014}, numpages = {12}, booktitle = {German Conference on Pattern Recognition (GCPR)}, volume = {36}, address = {Münster, Germany}, keywords = {space-time, 3d-reconstruction}, } @inproceedings{Oswald-Cremers-BMVC-2014, author = {M. R. Oswald and D. Cremers}, title = {Surface Normal Integration for Convex Space-time Multi-view Reconstruction}, booktitle = {British Machine Vision Conference (BMVC)}, year = {2014}, numpages = {11}, keywords = {space-time, 3d-reconstruction, surface normals, convex-relaxation}, } @inproceedings{Nieuwenhuis-et-al-eccv14, author = {C. Nieuwenhuis and S. Hawe and M. Kleinsteuber and D. Cremers}, title = {Co-Sparse Textural Similarity for Interactive Segmentation}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2014}, titleurl = {nieuwenhuis-et-al-eccv14.pdf}, keywords = {convex-relaxation, Segmentation}, } @inproceedings{Oswald-et-al-ECCV-2014, author = {M. R. Oswald and J. Stühmer and D. Cremers}, title = {Generalized Connectivity Constraints for Spatio-temporal 3D Reconstruction}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2014}, pages = {32-46}, numpages = {14}, keywords = {connectivity constraints, space-time, 3d-reconstruction, convex-relaxation}, topic = {3D Reconstruction, Shape Priors}, } @inproceedings{Strekalovskiy-Cremers-eccv14, author = {E. Strekalovskiy and D. Cremers}, title = {Real-Time Minimization of the Piecewise Smooth Mumford-Shah Functional}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2014}, pages = {127-141}, keywords = {convex-relaxation}, } @inproceedings{rodola-prmu14, author = {A. Kanezaki and E. Rodola and D. Cremers and T. Harada}, title = {対応点集合類似度学習を用いた剛体・非剛体物体検出 [Taiou tenshuugou ruijido gakushuu wo mochiita goutai-higoutai buttai kenshutsu]}, booktitle = {信学技報 - Pattern Recognition and Media Understanding (PRMU)}, year = {2014}, volume = {114}, number = {230}, pages = {13--18}, month = {oct}, } @inproceedings{andreux-nordia14, author = {M. Andreux and E. Rodola and M. Aubry and D. Cremers}, title = {Anisotropic Laplace-Beltrami Operators for Shape Analysis}, booktitle = {Sixth Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment (NORDIA)}, year = {2014}, titleurl = {andreux-nordia14.pdf}, } @inproceedings{dunkley14iros, author = {O. Dunkley and J. Engel and J. Sturm and D. Cremers}, title = {Visual-Inertial Navigation for a Camera-Equipped 25g Nano-Quadrotor}, booktitle = {IROS2014 Aerial Open Source Robotics Workshop}, year = {2014}, keywords = {nanocopter,vo,vio,vslam}, } @inproceedings{triebel14active, author = {R. Triebel and J. Stühmer and M. Souiai and D. Cremers}, title = {Active Online Learning for Interactive Segmentation Using Sparse Gaussian Processes}, booktitle = {German Conference on Pattern Recognition}, year = {2014}, topic = {Segmentation}, } @inproceedings{debnath14environment, author = {S. Debnath and S. S. Baishya and R. Triebel and V. Dutt and D. Cremers}, title = {Environment-adaptive Learning: How Clustering Helps to Obtain Good Training Data}, booktitle = {KI 2014: Advances in Artificial Intelligence}, pages = {68--79}, year = {2014}, editor = {Carsten Lutz and Michael Thielscher}, publisher = {Springer}, } @inproceedings{rodola-3dv14, author = {A. Kanezaki and E. Rodola and D. Cremers and T. Harada}, title = {Learning Similarities for Rigid and Non-Rigid Object Detection}, booktitle = {International Conference on 3D Vision ({3DV})}, year = {2014}, titleurl = {rodola-3dv14.pdf}, topic = {Correspondence, Shape Analysis, Shape Matching, Object Recognition, Machine Learning}, } @inproceedings{moellenhoff-et-al-15, author = {T. Möllenhoff and E. Strekalovskiy and M. Möller and D. Cremers}, title = {Low Rank Priors for Color Image Regularization}, booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, year = {2015}, titleurl = {moellenhoff_et_al_15.pdf}, } @inproceedings{bender_sdf2014, author = {D. Bender and M. Schikora and J. Sturm and D. Cremers}, title = {INS-Camera Calibration without Ground Control Points}, booktitle = {9th IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)}, year = {2014}, titleurl = {bender_sdf2014.pdf}, } @inproceedings{kerl143dv, author = {C. Kerl and M. Souiai and J. Sturm and D. Cremers}, title = {Towards Illumination-invariant 3D Reconstruction using ToF RGB-D Cameras}, booktitle = {International Conference on 3D Vision ({3DV})}, year = {2014}, keywords = {3d reconstruction,rgb-d,tof}, } @inproceedings{jaimez15icra, author = {M. Jaimez and M. Souiai and J. Gonzalez-Jimenez and D. Cremers}, title = {A Primal-Dual Framework for Real-Time Dense RGB-D Scene Flow}, booktitle = {Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA)}, year = {2015}, keywords = {scene-flow,rgb-d,primal-dual,real time}, titleurl = {jaimez_et_al_15.pdf}, } @inproceedings{jaimez2017icra, author = {M. Jaimez and C. Kerl and J. Gonzalez-Jimenez and D. Cremers}, title = {Fast Odometry and Scene Flow from RGB-D Cameras based on Geometric Clustering}, booktitle = {Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA)}, year = {2017}, titleurl = {jaimez_et_al_vosf_2017.pdf}, keywords = {rgb-d,scene-flow}, } @inproceedings{jaimez2017cvpr, author = {M. Jaimez and T. J. Cashman and A. Fitzgibbon and J. Gonzalez-Jimenez and D. Cremers}, title = {An Efficient Background Term for 3D Reconstruction and Tracking with Smooth Subdivision Surface Models}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2017}, titleurl = {jaimez_et_al_cvpr_2017.pdf}, keywords = {rgb-d, 3d reconstruction, tracking}, } @inproceedings{Stuehmer-Cremers-emmcvpr15, author = {J. Stühmer and D. Cremers}, title = {A Fast Projection Method for Connectivity Constraints in Image Segmentation}, booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, editor = {X.-C. Tai and E. Bae and T. F. Chan and M. Lysaker}, series = {LNCS}, year = {2015}, keywords = {image-segmentation,constrained convex optimization,shape-priors,medical imaging}, topic = {Segmentation, Shape Priors}, } @inproceedings{mecca-qcav15, author = {R. Mecca and E. Rodola and D. Cremers}, title = {Analysis of Surface Parametrizations for Modern Photometric Stereo Modeling}, booktitle = {International Conference on Quality Control by Artificial Vision (QCAV)}, year = {2015}, titleurl = {mecca-qcav15.pdf}, topic = {Photometric Stereo, 3D Reconstruction}, } @inproceedings{Schmidt-et-al-14, author = {F. R. Schmidt and T. Windheuser and U. Schlickewei and D. Cremers}, title = {Dense Elastic 3D Shape Matching}, booktitle = {Global Optimization Methods}, series = {LNCS}, volume = {8293}, pages = {1--18}, year = {2014}, publisher = {Springer}, titleurl = {schmidt-et-al-14.pdf}, } @inproceedings{bergamasco-cvpr15, author = {F. Bergamasco and A. Albarelli and L. Cosmo and A. Torsello and E. Rodola and D. Cremers}, title = {Adopting an Unconstrained Ray Model in Light-field Cameras for 3D Shape Reconstruction}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2015}, topic = {Calibration, Light Field, 3D Reconstruction}, } @article{rodola-prl15, author = {E. Rodola and A. Albarelli and D. Cremers and A. Torsello}, title = {A Simple and Effective Relevance-based Point Sampling for 3D Shapes}, journal = {Pattern Recognition Letters}, volume = {59}, number = {1}, year = {2015}, pages = {41--47}, publisher = {Elsevier}, titleurl = {relevance_sampling.zip}, topic = {Shape Analysis, Shape Matching}, } @article{mecca-cag15, author = {R. Mecca and E. Rodola and D. Cremers}, title = {Realistic Photometric Stereo Using Partial Differential Irradiance Equation Ratios}, volume = {51}, month = {Oct.}, pages = {8--16}, journal = {Computers and Graphics}, year = {2015}, publisher = {Elsevier}, topic = {Photometric Stereo, 3D Reconstruction}, doi = {10.1016/j.cag.2015.05.020}, } @inproceedings{mund15active, author = {D. Mund and R. Triebel and D. Cremers}, title = {Active Online Confidence Boosting for Efficient Object Classification}, booktitle = {Proc. IEEE International Conference on Robotics and Automation (ICRA)}, year = {2015}, } @article{moellenhoff-siims-15, author = {T. Möllenhoff and E. Strekalovskiy and M. Möller and D. Cremers}, title = {The Primal-Dual Hybrid Gradient Method for Semiconvex Splittings}, journal = {SIAM Journal on Imaging Sciences}, year = {2015}, volume = {8}, number = {2}, pages = {827-857}, titleurl = {moellenhoff_et_al_siims15.pdf}, } @inproceedings{Golkov-et-al-miccai2015-qDL, author = {V. Golkov and A. Dosovitskiy and P. Sämann and J. I. Sperl and T. Sprenger and M. Czisch and M. I. Menzel and P. A. Gómez and A. Haase and T. Brox and D. Cremers}, title = {{q-Space} Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion {MRI} Scans}, booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)}, month = {oct}, year = {2015}, address = {Munich, Germany}, keywords = {magnetic resonance imaging, diffusion MRI, deep learning, q-space deep learning, machine learning, model-free diffusion MRI, segmentation, medical imaging, deep learning}, } @inproceedings{flownet-iccv-15, author = {A. Dosovitskiy and P. Fischer and E. Ilg and P. Haeusser and C. Hazirbas and V. Golkov and P. van der Smagt and D. Cremers and T. Brox}, title = {{FlowNet: Learning Optical Flow with Convolutional Networks}}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, keywords = {deep learning, optical-flow}, year = {2015}, month = {dec}, doi = {10.1109/ICCV.2015.316}, } @incollection{Golkov-et-al-cdmri2015-holistic, author = {V. Golkov and J. M. Portegies and A. Golkov and R. Duits and D. Cremers}, title = {Holistic Image Reconstruction for Diffusion {MRI}}, booktitle = {Computational Diffusion {MRI}}, month = {oct}, publisher = {Springer}, year = {2015}, address = {Munich, Germany}, keywords = {magnetic resonance imaging, diffusion MRI, primal-dual, space of positions and orientations, medical imaging}, award = {Book Chapter, and Oral Presentation at {MICCAI} 2015 Workshop on Computational Diffusion {MRI}}, } @inproceedings{triebel15spencer, author = {R. Triebel and K. Arras and R. Alami and L. Beyer and S. Breuers and R. Chatila and M. Chetouani and D. Cremers and V. Evers and M. Fiore and H. Hung and O. A. I Ramírez and M. Joosse and H. Khambhaita and T. Kucner and B. Leibe and A. J. Lilienthal and T. Linder and M. Lohse and M. Magnusson and B. Okal and L. Palmieri and U. Rafi and M. van Rooij and L. Zhang}, title = {SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports}, booktitle = {Proc. Field and Service Robotics (FSR)}, year = {2015}, } @inproceedings{engel2015_stereo_lsdslam, author = {J. Engel and J. Stueckler and D. Cremers}, title = {Large-Scale Direct SLAM with Stereo Cameras}, booktitle = {International Conference on Intelligent Robots and Systems (IROS)}, year = {2015}, month = {sept}, keywords = {slam, stereo, semidense, reconstruction, vslam}, } @inproceedings{caruso2015_omni_lsdslam, author = {D. Caruso and J. Engel and D. Cremers}, title = {Large-Scale Direct SLAM for Omnidirectional Cameras}, booktitle = {International Conference on Intelligent Robots and Systems (IROS)}, month = {sept}, year = {2015}, keywords = {slam, fisheye, semidense, reconstruction, vslam}, } @inproceedings{tao15semi, author = {Y. Tao and R. Triebel and D. Cremers}, title = {Semi-supervised Online Learning for Efficient Classification of Objects in 3D Data Streams}, booktitle = {International Conference on Intelligent Robots and Systems (IROS)}, month = {sept}, year = {2015}, } @inproceedings{maier2015superresolution, author = {R. Maier and J. Stueckler and D. Cremers}, title = {Super-Resolution Keyframe Fusion for 3D Modeling with High-Quality Textures}, booktitle = {International Conference on 3D Vision ({3DV})}, month = {October}, year = {2015}, keywords = {rgb-d}, } @inproceedings{usenko15_3drecon_stereolsdslam, author = {V. Usenko and J. Engel and J. Stueckler and D. Cremers}, title = {Reconstructing Street-Scenes in Real-Time From a Driving Car}, booktitle = {Proc. of the Int. Conference on 3D Vision (3DV)}, month = {oct}, year = {2015}, keywords = {slam, stereo, semidense, reconstruction, vslam}, } @inproceedings{jaimez15_mocoop, author = {M. Jaimez and M. Souiai and J. Stueckler and J. Gonzalez-Jimenez and D. Cremers}, title = {Motion Cooperation: Smooth Piece-Wise Rigid Scene Flow from RGB-D Images}, booktitle = {Proc. of the Int. Conference on 3D Vision (3DV)}, month = {oct}, year = {2015}, keywords = {scene-flow,motion segmentation,rgb-d,primal-dual}, titleurl = {jaimez_et_al_3dv15.pdf}, } @inproceedings{rodola-vmv15, author = {E. Rodola and M. Moeller and D. Cremers}, title = {Point-wise Map Recovery and Refinement from Functional Correspondence}, booktitle = {Proceedings Vision, Modeling and Visualization (VMV)}, year = {2015}, address = {Aachen, Germany}, titleurl = {rodola-vmv15.pdf}, award = {Received the Best Paper Award}, topic = {Shape Analysis, Shape Matching}, } @inproceedings{kerl15iccv, author = {C. Kerl and J. Stueckler and D. Cremers}, title = {Dense Continuous-Time Tracking and Mapping with Rolling Shutter {RGB-D} Cameras}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2015}, address = {Santiago, Chile}, keywords = {rgb-d,slam,rgbdslam,dense visual odometry, vo, vslam}, } @inproceedings{souiai-iccv15, author = {M. Souiai and M. R. Oswald and Y. Kee and J. Kim and M. Pollefeys and D. Cremers}, title = {Entropy Minimization for Convex Relaxation Approaches}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2015}, address = {Santiago, Chile}, titleurl = {souiai-iccv15.pdf}, keywords = {convex relaxation,DC programming, image segmentation, 3D reconstruction}, } @article{KeeLYCK15, author = {Y. Kee and H. Lee and J. Yim and D. Cremers and J. Kim}, title = {Entropy Minimization for Groupwise Planar Shape Co-alignment and its Applications}, journal = {{IEEE} Signal Process. Lett.}, volume = {22}, number = {11}, pages = {1922--1926}, year = {2015}, } @inproceedings{stark-gcpr15, author = {F. Stark and C. Hazirbas and R. Triebel and D. Cremers}, title = {CAPTCHA Recognition with Active Deep Learning}, booktitle = {GCPR Workshop on New Challenges in Neural Computation}, year = {2015}, address = {Aachen, Germany}, keywords = {deep learning}, } @inproceedings{stuehmer-et-al-iccv2015, author = {J. Stühmer and S. Nowozin and A. Fitzgibbon and R. Szeliski and T. Perry and S. Acharya and D. Cremers and J. Shotton}, title = {Model-Based Tracking at 300Hz using Raw Time-of-Flight Observations}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2015}, address = {Santiago, Chile}, keywords = {rgb-d,tracking,3D reconstruction}, } @article{rodola-partial, author = {E. Rodola and L. Cosmo and M. M. Bronstein and A. Torsello and D. Cremers}, title = {Partial Functional Correspondence}, volume = {36}, number = {1}, pages = {222--236}, year = {2017}, journal = {Computer Graphics Forum}, publisher = {Wiley}, topic = {Shape Analysis, Shape Matching}, titleurl = {rodola-partial.pdf}, } @article{cosmo-multiway, author = {L. Cosmo and E. Rodola and A. Albarelli and F. Memoli and D. Cremers}, title = {Consistent Partial Matching of Shape Collections via Sparse Modeling}, year = {2017}, volume = {36}, number = {1}, pages = {209--221}, journal = {Computer Graphics Forum}, publisher = {Wiley}, topic = {Shape Analysis, Shape Matching}, titleurl = {cosmo-multiway.pdf}, } @article{boscaini-eg16, author = {D. Boscaini and J. Masci and E. Rodola and M. M. Bronstein and D. Cremers}, title = {Anisotropic Diffusion Descriptors}, year = {2016}, journal = {Computer Graphics Forum - Proc. EUROGRAPHICS}, publisher = {Wiley}, volume = {35}, number = {2}, pages = {431--441}, topic = {Shape Analysis, Shape Matching}, } @inproceedings{MayerIHFCDB16, author = {N. Mayer and E. Ilg and P. Häusser and P. Fischer and D. Cremers and A. Dosovitskiy and T. Brox}, title = {A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation}, booktitle = {2016 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2016, Las Vegas, NV, USA, June 27-30, 2016}, pages = {4040--4048}, publisher = {{IEEE} Computer Society}, year = {2016}, } @inproceedings{Golkov-et-al-isbi2016, author = {V. Golkov and T. Sprenger and J. I. Sperl and M. I. Menzel and M. Czisch and P. Sämann and D. Cremers}, title = {Model-Free Novelty-Based Diffusion {MRI}}, booktitle = {{IEEE} International Symposium on Biomedical Imaging ({ISBI})}, month = {apr}, year = {2016}, address = {Prague, Czech Republic}, keywords = {magnetic resonance imaging, diffusion MRI, novelty detection, q-space, machine learning, model-free diffusion MRI, segmentation, medical imaging}, } @inproceedings{Golkov-et-al-nips2016, author = {V. Golkov and M. J. Skwark and A. Golkov and A. Dosovitskiy and T. Brox and J. Meiler and D. Cremers}, title = {Protein Contact Prediction from Amino Acid Co-Evolution Using Convolutional Networks for Graph-Valued Images}, booktitle = {Annual Conference on Neural Information Processing Systems (NIPS)}, month = {dec}, year = {2016}, address = {Barcelona, Spain}, keywords = {computational structural biology, deep learning, convolutional networks, graph-valued images, deep learning, biology}, award = {Oral Presentation (acceptance rate: under 2%)}, } @article{Golkov-et-al-tmi2016, author = {V. Golkov and A. Dosovitskiy and J. I. Sperl and M. I. Menzel and M. Czisch and P. Sämann and T. Brox and D. Cremers}, title = {{q-Space} Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion {MRI} Scans}, year = {2016}, journal = {IEEE Transactions on Medical Imaging}, volume = {35}, issue = {5}, keywords = {magnetic resonance imaging, diffusion MRI, deep learning, q-space deep learning, machine learning, model-free diffusion MRI, segmentation, medical imaging, deep learning}, issuetitle = {Special Issue on Deep Learning}, award = {Special Issue on Deep Learning}, } @inproceedings{LRSBC16, author = {Z. Lähner and E. Rodola and F. R. Schmidt and M. M. Bronstein and D. Cremers}, title = {Efficient Globally Optimal 2D-to-3D Deformable Shape Matching}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {May}, year = {2016}, keywords = {Shape Analysis, Geometry Processing}, url = {http://vision.in.tum.de/~laehner/Elastic2D3D/}, } @inproceedings{usenko16icra, title = {Direct Visual-Inertial Odometry with Stereo Cameras}, author = {V. Usenko and J. Engel and J. Stueckler and D. Cremers}, booktitle = {International Conference on Robotics and Automation (ICRA)}, year = {2016}, month = {May}, keywords = {visual inertial odometry, sensor fusion, vio, vslam}, } @inproceedings{narr16stream, title = {Stream-based Active Learning for Efficient and Adaptive Classification of 3D Objects}, author = {A. Narr and R. Triebel and D. Cremers}, booktitle = {International Conference on Robotics and Automation (ICRA)}, year = {2016}, month = {May}, } @inproceedings{SHREC16-3dor, title = {SHREC’16: Matching of Deformable Shapes with Topological Noise}, author = {Z. Lähner and E. Rodola and M. M. Bronstein and D. Cremers and O. Burghard and L. Cosmo and A. Dieckmann and R. Klein and Y. Sahillioglu}, booktitle = {Proc. of Eurographics Workshop on 3D Object Retrieval (3DOR)}, year = {2016}, month = {May}, topic = {Shape Analysis}, url = {http://vision.in.tum.de/~laehner/shrec2016/}, keywords = {Shape Analysis, Shape Matching, SHREC, Topology, Geometry Processing}, } @inproceedings{shrec16-partial, title = {SHREC’16: Partial Matching of Deformable Shapes}, author = {L. Cosmo and E. Rodola and M. M. Bronstein and A. Torsello and D. Cremers and Y. Sahillioglu}, booktitle = {Proc. of Eurographics Workshop on 3D Object Retrieval (3DOR)}, year = {2016}, month = {May}, keywords = {Shape Analysis, Shape Matching, SHREC}, } @inproceedings{moellenhoff-laude-cvpr16, author = {T. Möllenhoff and E. Laude and M. Moeller and J. Lellmann and D. Cremers}, title = {Sublabel-Accurate Relaxation of Nonconvex Energies}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2016}, titleurl = {moellenhoff_laude_cvpr_16.pdf}, keywords = {convex-relaxation}, award = {Oral Presentation, Received the Best Paper Honorable Mention Award at CVPR 2016}, } @inproceedings{lingni17iros, author = {L. Ma and J. Stueckler and C. Kerl and D. Cremers}, title = {Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras}, booktitle = {International Conference on Intelligent Robots and Systems (IROS)}, address = {Vancouver, Canada}, year = {2017}, month = {Sep}, keywords = {CNN, semantic segmentation, multi-view}, } @inproceedings{lingni16icra, author = {L. Ma and C. Kerl and J. Stueckler and D. Cremers}, title = {CPA-SLAM: Consistent Plane-Model Alignment for Direct RGB-D SLAM}, booktitle = {International Conference on Robotics and Automation (ICRA)}, year = {2016}, month = {May}, keywords = {RGB-D SLAM, semantic segmentation, vslam}, } @article{litany16, author = {O. Litany and E. Rodola and A. M. Bronstein and M. M. Bronstein and D. Cremers}, title = {Non-Rigid Puzzles}, journal = {Computer Graphics Forum}, volume = {35}, number = {5}, pages = {135--143}, award = {Received the Best Paper Award at SGP 2016}, year = {2016}, publisher = {Wiley}, topic = {Shape Analysis, Segmentation}, titleurl = {litany16.pdf}, } @inproceedings{engel2016monodataset, author = {J. Engel and V. Usenko and D. Cremers}, title = {A Photometrically Calibrated Benchmark For Monocular Visual Odometry}, booktitle = {arXiv:1607.02555}, arxiv = { arXiv:1607.02555}, year = {2016}, month = {July}, keywords = {mono-ds,dso,photometric-calibration, vo, vslam}, } @inproceedings{engel2016dso, author = {J. Engel and V. Koltun and D. Cremers}, title = { Direct Sparse Odometry}, booktitle = {arXiv:1607.02565}, arxiv = { arXiv:1607.02565}, year = {2016}, month = {July}, keywords = {mono-ds,dso, vslam}, } @article{Engel-et-al-pami2018, author = {J. Engel and V. Koltun and D. Cremers}, title = { Direct Sparse Odometry}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2018}, month = {mar}, titleurl = {engel_et_al_pami2018.pdf}, keywords = {mono-ds,dso,directsparseodometry, vslam}, } @inproceedings{laude16eccv, author = {E. Laude and T. Möllenhoff and M. Moeller and J. Lellmann and D. Cremers}, title = {Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies}, year = {2016}, month = {October}, booktitle = {European Conference on Computer Vision (ECCV)}, keywords = {convex-optimization, convex-relaxation, multilabeling, primal-dual}, } @inproceedings{BenderCK16, author = {D. Bender and D. Cremers and W. Koch}, title = {A position free boresight calibration for INS-camera systems}, booktitle = {2016 {IEEE} International Conference on Multisensor Fusion and Integration for Intelligent Systems, {MFI} 2016, Baden-Baden, Germany, September 19-21, 2016}, pages = {52--57}, publisher = {{IEEE}}, year = {2016}, } @inproceedings{chiotellis2016csdlmnn, author = {I. Chiotellis and R. Triebel and T. Windheuser and D. Cremers}, title = {Non-Rigid 3D Shape Retrieval via Large Margin Nearest Neighbor Embedding}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2016}, month = {October}, keywords = {Geometry Processing}, } @inproceedings{hazirbasma2016fusenet, author = {C. Hazirbas and L. Ma and C. Domokos and D. Cremers}, title = {{FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture}}, booktitle = {Asian Conference on Computer Vision}, year = {2016}, month = {november}, keywords = {segmentation, deep learning}, } @inproceedings{windheusercremers2016, author = {T. Windheuser and D. Cremers}, title = {A Convex Solution to Spatially-Regularized Correspondence Problems}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2016}, month = {October}, topic = {Shape Analysis, Convex Relaxation Methods, Correspondence}, keywords = {Geometry Processing}, } @article{vestner2016bayesian, title = {Bayesian Inference of Bijective Non-Rigid Shape Correspondence}, author = {M. Vestner and R. Litman and A. Bronstein and E. Rodola and D. Cremers}, journal = {arXiv preprint arXiv:1607.03425}, year = {2016}, titleurl = {2016_Bayesian_Inference_Shape.pdf}, topic = {Shape Analysis, Shape Matching}, } @inproceedings{vestner2017pmf, title = {Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel Density Estimation in the Product Space}, author = {M. Vestner and R. Litman and E. Rodola and A. Bronstein and D. Cremers}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2017}, titleurl = {2017_ProductManifoldFilter.pdf}, topic = {Shape Analysis, Shape Matching}, keywords = {Geometry Processing}, } @inproceedings{dzitsiuk2017icra, title = {De-noising, Stabilizing and Completing {3D} Reconstructions On-the-go using Plane Priors}, author = {M. Dzitsiuk and J. Sturm and R. Maier and L. Ma and D. Cremers}, booktitle = {International Conference on Robotics and Automation (ICRA)}, year = {2017}, month = {May}, keywords = {rgb-d,slam,rgbdslam,reconstruction,3d-reconstruction,fusion}, } @inproceedings{stumberg16exploration, author = {L. von Stumberg and V. Usenko and J. Engel and J. Stueckler and D. Cremers}, title = {From Monocular {SLAM} to Autonomous Drone Exploration}, booktitle = {European Conference on Mobile Robots (ECMR)}, arxiv = {arXiv:1609.07835}, year = {2017}, month = {September}, keywords = {vslam}, } @inproceedings{walch16spatialstms, author = {F. Walch and C. Hazirbas and L. Leal-Taixé and T. Sattler and S. Hilsenbeck and D. Cremers}, title = {Image-based localization using LSTMs for structured feature correlation}, month = {October}, year = {2017}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, keywords = {deep learning}, } @incollection{vestner2016applying, title = {Applying Random Forests to the Problem of Dense Non-rigid Shape Correspondence}, author = {M. Vestner and E. Rodolà and T. Windheuser and RBS. Bulò and D. Cremers}, booktitle = {Perspectives in Shape Analysis}, pages = {231--248}, year = {2016}, publisher = {Springer}, keywords = {Geometry Processing}, } @inproceedings{sahand2016drive, author = {S. Sharifzadeh and I. Chiotellis and R. Triebel and D. Cremers}, title = {Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks}, year = {2016}, publisher = {NIPS Workshops}, month = {December}, keywords = {autonomous driving, reinforcement learning, artificial intelligence, DQN, deep learning}, } @inproceedings{Peeken-et-al-2017, author = {J.C. Peeken and C. Knie and V. Golkov and K. Kessel and F. Pasa and Q. Khan and M. Seroglazov and J. Kukačka and T. Goldberg and L. Richter and J. Reeb and B. Rost and F. Pfeiffer and D. Cremers and F. Nüsslin and S.E. Combs}, title = {Establishment of an interdisciplinary workflow of machine learning-based Radiomics in sarcoma patients}, year = {2017}, booktitle = {23. Jahrestagung der Deutschen Gesellschaft für Radioonkologie (DEGRO)}, keywords = {deep learning, medical imaging}, } @inproceedings{Golkov-et-al-arxiv2017-function3d, author = {V. Golkov and M. J. Skwark and A. Mirchev and G. Dikov and A. R. Geanes and J. Mendenhall and J. Meiler and D. Cremers}, title = {{3D} Deep Learning for Biological Function Prediction from Physical Fields}, booktitle = {International Conference on 3D Vision (3DV)}, year = {2020}, journal = {arXiv preprint arXiv:1704.04039}, eprint = {1704.04039}, eprinttype = {arXiv}, keywords = {computational structural biology, deep learning, convolutional networks, protein function, QSAR, deep learning, biology}, } @inproceedings{Queau2017Micro, author = {Y. Quéau and M. Pizenberg and J.-D. Durou and D. Cremers}, title = {Microgeometry capture and RGB albedo estimation by photometric stereo without demosaicing}, booktitle = {International Conference on Quality Control by Artificial Vision (QCAV)}, year = {2017}, titleurl = {qcav2017_microgeometry.pdf}, } @inproceedings{haeusser-cvpr17, author = {P. Haeusser and A. Mordvintsev and D. Cremers}, title = {Learning by Association - A versatile semi-supervised training method for neural networks}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2017}, keywords = {semi-supervised, deep learning, neural networks, association, associative_learning}, titleurl = {haeusser_cvpr_17.pdf}, } @inproceedings{slavcheva-et-al-cvpr17, author = {M. Slavcheva and M. Baust and D. Cremers and S. Ilic}, title = {KillingFusion: Non-rigid 3D Reconstruction without Correspondences}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2017}, titleurl = {slavcheva_et_al_cvpr17.pdf}, } @inproceedings{usenko17replanning, author = {V. Usenko and L. von Stumberg and A. Pangercic and D. Cremers}, title = {Real-Time Trajectory Replanning for MAVs using Uniform B-splines and a 3D Circular Buffer}, booktitle = {International Conference on Intelligent Robots and Systems (IROS)}, address = {Vancouver, Canada}, year = {2017}, month = {Sep}, award = {Best Paper Award - Finalist ({link})}, keywords = {trajectory, replanning, MAV}, } @inproceedings{BenderRSCK16, author = {D. Bender and F. Rouatbi and M. Schikora and D. Cremers and W. Koch}, title = {Scaling the world of monocular {SLAM} with INS-measurements for {UAS} navigation}, booktitle = {19th International Conference on Information Fusion, {FUSION} 2016, Heidelberg, Germany, July 5-8, 2016}, pages = {1493--1500}, publisher = {{IEEE}}, year = {2016}, keywords = {vslam}, } @inproceedings{QueauSSVM2017, address = {Kolding, Denmark}, author = {Y. Quéau and T. Wu and D. Cremers}, booktitle = {International Conference on Scale Space and Variational Methods in Computer Vision (SSVM)}, %pages = {}, %series = {Lecture Notes in Computer Science}, title = {{Semi-Calibrated Near-Light Photometric Stereo}}, %volume = {}, year = {2017}, addendum = {(To appear)}, titleurl = {ssvm_PS.pdf}, } @inproceedings{MelouSSVM2017, address = {Kolding, Denmark}, author = {J. Mélou and Y. Quéau and J.-D. Durou and F. Castan and D Cremers}, booktitle = {International Conference on Scale Space and Variational Methods in Computer Vision (SSVM)}, %pages = {}, %series = {Lecture Notes in Computer Science}, title = {{Beyond Multi-view Stereo: Shading-Reflectance Decomposition}}, %volume = {}, year = {2017}, addendum = {(To appear)}, titleurl = {SSVM2017.pdf}, } @inproceedings{QueauCVPR2017, address = {Honlulu, USA}, author = {Y. Quéau and T. Wu and F. Lauze and J.-D. Durou and D. Cremers}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, title = {{A Non-Convex Variational Approach to Photometric Stereo under Inaccurate Lighting}}, year = {2017}, titleurl = {camera_Ready-robust_PS.pdf}, } @inproceedings{meinhardt17learning, author = {T. Meinhardt and M. Moeller and C. Hazirbas and D. Cremers}, title = {Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems}, month = {October}, year = {2017}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, keywords = {deep learning}, } @inproceedings{hazirbas18ddff, author = {C. Hazirbas and S. G. Soyer and M. C. Staab and L. Leal-Taixé and D. Cremers}, title = {{Deep Depth From Focus}}, year = {2018}, month = {December}, booktitle = {Asian Conference on Computer Vision (ACCV)}, keywords = {deep learning}, } @inproceedings{osvosCVPR2017, address = {Honolulu, USA}, author = {S. Caelles and K.-K. Maninis and J. Pont-Tuset and L. Leal-Taixé and D. Cremers and L. V Gool}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, title = {{One-Shot Video Object Segmentation}}, year = {2017}, keywords = {deep learning}, } @inproceedings{QueauMVSFS2017, author = {Y. Quéau and J. Mélou and J.-D. Durou and D. Cremers}, title = {{Dense Multi-view 3D-reconstruction Without Dense Correspondences}}, year = {2017}, booktitle = {ArXiv preprint 1704.00337}, titleurl = {TechRep_MV_SfS.pdf}, } @article{yang18challenges, author = {N. Yang and R. Wang and X. Gao and D. Cremers}, title = {Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect}, journal = { In IEEE Robotics and Automation Letters (RA-L) & Int. Conference on Intelligent Robots and Systems (IROS)}, volume = {3}, issue = {4}, pages = {2878--2885}, year = {2018}, month = {Oct}, doi = {10.1109/LRA.2018.2846813}, titleurl = {yang18challenges.pdf}, keywords = {Brightness;Calibration;Cameras;Feature extraction;Optimization;Robustness;Simultaneous localization and mapping;Localization;SLAM;performance evaluation and benchmarking;vslam}, } @article{krieg2017genetic, title = {Genetic defects in ß-spectrin and tau sensitize C. elegans axons to movement-induced damage via torque-tension coupling}, author = {M. Krieg and J. Stühmer and J. G. Cueva and R. Fetter and K. Spilker and D. Cremers and K. Shen and A. R. Dunn and M. B. Goodman}, journal = {eLife}, volume = {6}, pages = {e20172}, year = {2017}, publisher = {eLife Sciences Publications Limited}, keywords = {biology}, } @article{krieg2017tau, title = {Tau Like Proteins Reduce Torque Generation in Microtubule Bundles}, author = {M. Krieg and J. Stühmer and J. G. Cueva and R. Fetter and K. Spilker and D. Cremers and K. Shen and A. R. Dunn and M. B. Goodman}, journal = {Biophysical Journal}, volume = {112}, number = {3}, pages = {29a--30a}, year = {2017}, publisher = {Elsevier}, keywords = {biology}, } @inproceedings{haeusser_iccv_17, author = {P. Haeusser and T. Frerix and A. Mordvintsev and D. Cremers}, title = {Associative Domain Adaptation}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2017}, keywords = {semi-supervised, deep learning, neural networks, association, domain adaptation, associative_learning}, titleurl = {haeusser_iccv_17.pdf}, } @inproceedings{QueauGRETSI2017, address = {Juan-les-Pins, USA}, author = {Y. Quéau and M. Pizenberg and D. Cremers and J.-D. Durou}, booktitle = {GRETSI}, title = {{Stéréophotométrie microscopique sans démosaïquage}}, year = {2017}, titleurl = {gretsifr.pdf}, } @article{QueauPS2017, author = {Y. Quéau and B. Durix and T. Wu and D. Cremers and F. Lauze and J.-D. Durou}, title = {{LED-based Photometric Stereo: Modeling, Calibration and Numerical Solution}}, year = {2018}, volume = {60}, number = {3}, pages = {313--340}, titleurl = {JMIV_LEDs.pdf}, journal = {Journal of Mathematical Imaging and Vision}, doi = {10.1007/s10851-017-0761-1}, } @inproceedings{kernel17, author = {M. Vestner and Z. Lähner and A. Boyarski and O. Litany and R. Slossberg and T. Remez and E. Rodolà and A. M. Bronstein and M. M. Bronstein and R. Kimmel and D. Cremers}, title = {Efficient Deformable Shape Correspondence via Kernel Matching}, booktitle = {International Conference on 3D Vision (3DV)}, year = {2017}, month = {October}, address = {Qingdao, China}, award = {Oral Presentation}, keywords = {Shape Analysis, Shape Matching, Geometry Processing}, } @inproceedings{maier2017efficient, title = {Efficient Online Surface Correction for Real-time Large-Scale {3D} Reconstruction}, author = {R. Maier and R. Schaller and D. Cremers}, booktitle = {British Machine Vision Conference (BMVC)}, year = {2017}, month = {September}, address = {London, United Kingdom}, keywords = {rgb-d,slam,rgbdslam,rgb-d benchmark,reconstruction,3d-reconstruction,fusion,vslam}, } @inproceedings{GeipingDC017, author = {J. Geiping and H. Dirks and D. Cremers}, editor = {Marcello Pelillo and Edwin R. Hancock}, title = {Multiframe Motion Coupling for Video Super Resolution}, booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition - 11th International Conference, {EMMCVPR} 2017, Venice, Italy, October 30 - November 1, 2017, Revised Selected Papers}, series = {Lecture Notes in Computer Science}, volume = {10746}, pages = {123--138}, publisher = {Springer}, year = {2017}, } @inproceedings{maier2017intrinsic3d, title = {Intrinsic3D: High-Quality {3D} Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting}, author = {R. Maier and K. Kim and D. Cremers and J. Kautz and M. Niessner}, booktitle = {International Conference on Computer Vision (ICCV)}, year = {2017}, month = {October}, address = {Venice, Italy}, keywords = {rgb-d,reconstruction,3d-reconstruction,fusion,intrinsic3d}, } @inproceedings{sang2020wacv, title = {Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach}, author = {L. Sang and B. Haefner and D. Cremers}, booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)}, month = {March}, address = {Colorado, USA}, year = {2020}, doi = {10.1109/WACV45572.2020.9093491}, eprint = {1912.06501}, eprinttype = {arXiv}, eprintclass = {cs.CV}, award = {Spotlight Presentation}, titleurl = {sang2020wacv.pdf}, keywords = {3d-reconstruction,rgb-d,photometry}, } @inbook{maier2020rgbdvision, title = {{RGB-D Vision}}, author = {R. Maier and D. Cremers}, chapter = {Encyclopedia of Robotics}, editor = {Ang, M.H. and Khatib, O. and Siciliano, B.}, year = {2020}, publisher = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, pages = {1--11}, isbn = {978-3-642-41610-1}, doi = {10.1007/978-3-642-41610-1_109-1}, url = {https://doi.org/10.1007/978-3-642-41610-1_109-1}, keywords = {rgbd, vslam}, } @inbook{brahimi2020springer, title = {On the Well-Posedness of Uncalibrated Photometric Stereo Under General Lighting}, author = {M Brahimi and Y Quéau and B Haefner and D Cremers}, editor = {Durou, Jean-Denis and Falcone, Maurizio and Qu{\'e}au, Yvain and Tozza, Silvia}, booktitle = {Advances in Photometric 3D-Reconstruction}, year = {2020}, publisher = {Springer International Publishing}, address = {Cham}, pages = {147--176}, isbn = {978-3-030-51866-0}, doi = {10.1007/978-3-030-51866-0_5}, eprint = {1911.07268}, eprinttype = {arXiv}, eprintclass = {cs.CV}, titleurl = {brahimi2020springer.pdf}, keywords = {photometry}, } @inproceedings{brahimi2022arxiv, title = {{SupeRVol: Super-Resolution Shape and Reflectance Estimation in Inverse Volume Rendering}}, author = {M Brahimi and B Haefner and T Yenamandra and B Goldluecke and D Cremers}, booktitle = {{IEEE Winter Conference on Applications of Computer Vision (WACV)}}, month = {January}, address = {Hawaii, USA}, year = {2024}, eprint = {2212.04968}, eprinttype = {arXiv}, eprintclass = {cs.CV}, titleurl = {brahimi2022arxiv.pdf}, keywords = {d-reconstruction,photometry,deep learning,geometry processing}, } @inproceedings{DykeSLRABBBCFGG19, author = {R. Dyke and C. Stride and Y.-K. Lai and P. L. Rosin and M. Aubry and A. Boyarski and A. M. Bronstein and M. M. Bronstein and D. Cremers and M. Fisher and T. Groueix and D. Guo and V. G. Kim and R. Kimmel and Z. Lähner and K. Li and O. Litany and T. Remez and E. Rodolà and B. C. Russell and Y. Sahillioglu and R. Slossberg and G. K. L. Tam and M. Vestner and Z. Wu and J. Yang}, editor = {Silvia Biasotti and Guillaume Lavou{\'{e}} and Remco C. Veltkamp}, title = {Shape Correspondence with Isometric and Non-Isometric Deformations}, booktitle = {12th Eurographics Workshop on 3D Object Retrieval, 3DOR@Eurographics 2019, Genoa, Italy, May 5-6, 2019}, pages = {111--119}, publisher = {Eurographics Association}, year = {2019}, keywords = {Geometry Processing}, } @inproceedings{haefner20193dv, title = {Photometric Segmentation: Simultaneous Photometric Stereo and Masking}, author = {B. Haefner and Y. Quéau and D. Cremers}, booktitle = {International Conference on 3D Vision (3DV)}, month = {September}, address = {Québec City, Canada}, year = {2019}, doi = {10.1109/3DV.2019.00033}, award = {Spotlight Presentation}, titleurl = {haefner20193dv.pdf}, keywords = {photometry}, } @inproceedings{haefner20213dv, title = {Recovering Real-world Reflectance Properties and Shading from HDR Imagery}, author = {B. Haefner and S. Green and A. Oursland and D. Andersen and M. Goesele and D. Cremers and R. Newcombe and T. Whelan}, booktitle = {International Conference on 3D Vision (3DV)}, year = {2021}, doi = {10.1109/3DV53792.2021.00115}, titleurl = {haefner20213dv.pdf}, award = {Spotlight Presentation}, keywords = {photometry}, } @inproceedings{haefner2019iccv, title = {Variational Uncalibrated Photometric Stereo under General Lighting}, author = {B. Haefner and Z. Ye and M. Gao and T. Wu and Y. Quéau and D. Cremers}, booktitle = {IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, address = {Seoul, South Korea}, eprint = {1904.03942}, eprinttype = {arXiv}, eprintclass = {cs.CV}, year = {2019}, doi = {10.1109/ICCV.2019.00863}, titleurl = {haefner2019iccv.pdf}, keywords = {d-reconstruction,photometry,variational}, } @article{laude2020jota, title = {Bregman Proximal Mappings and Bregman-Moreau Envelopes under Relative Prox-Regularity}, author = {E. Laude and P. Ochs and D. Cremers}, journal = {Journal of Optimization Theory and Applications}, volume = {184}, number = {3}, pages = {724-761}, year = {2020}, eprint = {1907.04306}, eprinttype = {arXiv}, eprintclass = {math.OC}, } @article{haefner2020tpami, title = {Photometric Depth Super-Resolution}, author = {B. Haefner and S. Peng and A. Verma and Y. Quéau and D. Cremers}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)}, year = {2020}, volume = {42}, number = {10}, pages = {2453-2464}, doi = {10.1109/TPAMI.2019.2923621}, eprint = {1809.10097}, eprinttype = {arXiv}, eprintclass = {cs.CV}, titleurl = {haefner2020tpami.pdf}, keywords = {rgb-d,reconstruction,3d-reconstruction,photometry,variational,super-resolution,photometricdepthsr}, } @article{MoellerBSC15, author = {M. Möller and M. Benning and C. Schönlieb and D. Cremers}, title = {Variational Depth From Focus Reconstruction}, journal = {{IEEE} Trans. Image Process.}, volume = {24}, number = {12}, pages = {5369--5378}, year = {2015}, } @inproceedings{haefner2018cvpr, title = {Fight ill-posedness with ill-posedness: Single-shot variational depth super-resolution from shading}, author = {B. Haefner and Y. Quéau and T. Möllenhoff and D. Cremers}, booktitle = {I{EEE}/{CVF} {C}onference on {C}omputer {V}ision and {P}attern {R}ecognition (CVPR)}, year = {2018}, doi = {10.1109/CVPR.2018.00025}, titleurl = {haefner2018cvpr.pdf}, award = {Spotlight Presentation}, keywords = {rgb-d,reconstruction,3d-reconstruction,photometry,variational,super-resolution,photometricdepthsr}, } @inproceedings{peng2017, title = {Depth Super-Resolution Meets Uncalibrated Photometric Stereo}, author = {S. Peng and B. Haefner and Y. Quéau and D. Cremers}, booktitle = {{IEEE International Conference on Computer Vision Workshops (ICCVW)}}, year = {2017}, doi = {10.1109/ICCVW.2017.349}, eprint = {1708.00411}, eprinttype = {arXiv}, eprintclass = {cs.CV}, award = {Oral Presentation at ICCV Workshop on Color and Photometry in Computer Vision}, titleurl = {peng2017iccvw.pdf}, keywords = {rgb-d,reconstruction,3d-reconstruction,photometry,variational,super-resolution,photometricdepthsr}, } @inproceedings{wang2017stereoDSO, title = {Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras}, author = {R. Wang and M. Schwörer and D. Cremers}, booktitle = {International Conference on Computer Vision (ICCV)}, year = {2017}, month = {October}, address = {Venice, Italy}, keywords = {dso, visual odometry, stereo, 3D reconstruction, stereodso}, } @inproceedings{moellenhoff-iccv-2017, title = {Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity Problems}, author = {T. Möllenhoff and D. Cremers}, booktitle = {International Conference on Computer Vision (ICCV)}, year = {2017}, month = {October}, address = {Venice, Italy}, keywords = {convex-relaxation}, } @article{RodolaMC17, author = {E Rodolà and M Möller and D Cremers}, title = {Regularized Pointwise Map Recovery from Functional Correspondence}, journal = {Comput. Graph. Forum}, volume = {36}, number = {8}, pages = {700--711}, year = {2017}, keywords = {Geometry Processing}, } @article{BringmannCKM18, author = {B Bringmann and D Cremers and F Krahmer}, title = {The homotopy method revisited: Computing solution paths of L1-regularized problems}, journal = {Math. Comput.}, volume = {87}, number = {313}, pages = {2343--2364}, year = {2018}, } @article{Melou2017_reflectance, author = {J. Mélou and Y. Quéau and J.-D. Durou and F. Castan and D. Cremers}, title = {{Variational Reflectance Estimation from Multi-view Images}}, doi = {10.1007/s10851-018-0809-x}, volume = {60}, number = {9}, pages = {1527--1546}, year = {2018}, titleurl = {JMIV_reflectance.pdf}, journal = {Journal of Mathematical Imaging and Vision}, } @inproceedings{Queau2017_SfS, author = {Y. Quéau and J. Mélou and F. Castan and D. Cremers and J.-D. Durou}, title = {{A Variational Approach to Shape-from-shading Under Natural Illumination}}, year = {2017}, booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, titleurl = {emmcvpr_sfs.pdf}, doi = {10.1007/978-3-319-78199-0_23}, } @article{bergmann_RA-L_2018, author = {P. Bergmann and R. Wang and D. Cremers}, title = {Online Photometric Calibration of Auto Exposure Video for Realtime Visual Odometry and SLAM}, journal = {IEEE Robotics and Automation Letters (RA-L)}, volume = {3}, issue = {2}, pages = {627--634}, year = {2018}, month = {April}, titleurl = {bergmann17calibration.pdf}, keywords = {visual odometry, calibration, SLAM, direct, dso, photometric-calibration}, award = {ICRA'18 Best Vision Paper Award - Finalist}, } @inproceedings{laude-et-al-transductive, author = {E. Laude and J.-H. Lange and J. Schüpfer and C. Domokos and L. Leal-Taixé and F. R. Schmidt and B. Andres and D. Cremers}, title = {Discrete-Continuous {ADMM} for Transductive Inference in Higher-Order {MRF}s}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2018}, titleurl = {laude-2018-discrete-continuous.pdf}, } @inproceedings{DomokosSC18, author = {C Domokos and FR. Schmidt and D Cremers}, editor = {Vittorio Ferrari and Martial Hebert and Cristian Sminchisescu and Yair Weiss}, title = {{MRF} Optimization with Separable Convex Prior on Partially Ordered Labels}, booktitle = {Computer Vision - {ECCV} 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part {VIII}}, series = {Lecture Notes in Computer Science}, volume = {11212}, pages = {341--356}, publisher = {Springer}, year = {2018}, } @inproceedings{laude-et-al-nonconvex-moreau-17, author = {E. Laude and T. Wu and D. Cremers}, title = {A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization}, booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)}, year = {2018}, titleurl = {laude-2018-proximal.pdf}, } @inproceedings{moellenhoff-et-al-combinatorial-18, author = {T. Möllenhoff and Z. Ye and T. Wu and D. Cremers}, title = {Combinatorial Preconditioners for Proximal Algorithms on Graphs}, booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)}, year = {2018}, titleurl = {moellenhoff-et-al-combinatorial-18.pdf}, } @inproceedings{Bernard-et-al-2017, author = {F. Bernard and F. R. Schmidt and J. Thunberg and D. Cremers}, title = {A Combinatorial Solution to Non-Rigid 3D Shape-to-Image Matching}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2017}, keywords = {Geometry Processing}, } @inproceedings{SconaJPFC18, author = {R Scona and M Jaimez and YR. Petillot and M Fallon and D Cremers}, title = {StaticFusion: Background Reconstruction for Dense {RGB-D} {SLAM} in Dynamic Environments}, booktitle = {2018 {IEEE} International Conference on Robotics and Automation, {ICRA} 2018, Brisbane, Australia, May 21-25, 2018}, pages = {1--9}, publisher = {{IEEE}}, year = {2018}, keywords = {vslam}, } @article{Kukacka-et-al-2017, author = {J. Kukačka and V. Golkov and D. Cremers}, title = {Regularization for Deep Learning: A Taxonomy}, year = {2017}, journal = {arXiv preprint arXiv:1710.10686}, eprint = {1710.10686}, eprinttype = {arXiv}, keywords = {deep learning, neural networks, regularization, data augmentation, network architecture, loss function, dropout, residual learning, optimization}, } @inproceedings{Golkov-et-al-ismrm2018-novelty, author = {V. Golkov and A. Vasilev and F. Pasa and I. Lipp and W. Boubaker and E. Sgarlata and F. Pfeiffer and V. Tomassini and D. K. Jones and D. Cremers}, title = {{q-Space} Novelty Detection in Short Diffusion {MRI} Scans of Multiple Sclerosis}, year = {2018}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, keywords = {novelty detection, anomaly detection, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, segmentation}, } @inproceedings{Vasilev-et-al-2018, author = {A. Vasilev and V. Golkov and M. Meissner and I. Lipp and E. Sgarlata and V. Tomassini and D. K. Jones and D. Cremers}, title = {{q}-{S}pace Novelty Detection with Variational Autoencoders}, year = {2019}, booktitle = {{MICCAI} 2019 International Workshop on Computational Diffusion {MRI}}, eprint = {1806.02997}, eprinttype = {arXiv}, keywords = {deep learning, novelty detection, anomaly detection, neural networks, medical imaging, magnetic resonance imaging, diffusion MRI}, award = {Oral Presentation}, } @inproceedings{Swazinna-et-al-ismrm2019, author = {P. Swazinna and V. Golkov and I. Lipp and E. Sgarlata and V. Tomassini and D. K. Jones and D. Cremers}, title = {Negative-Unlabeled Learning for Diffusion {MRI}}, year = {2019}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, keywords = {deep learning, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, weakly-supervised learning, positive-unlabeled learning, semi-supervised learning, localization}, } @inproceedings{Golkov-et-al-ismrm2018-global, author = {V. Golkov and P. Swazinna and M. M. Schmitt and Q. A. Khan and C. M. W. Tax and M. Serahlazau and F. Pasa and F. Pfeiffer and G. J. Biessels and A. Leemans and D. Cremers}, title = {{q-Space} Deep Learning for {A}lzheimer's Disease Diagnosis: Global Prediction and Weakly-Supervised Localization}, year = {2018}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, keywords = {deep learning, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, weakly-supervised learning, localization}, } @article{Do-et-al-2018-pre-miRNA, author = {B. T. Do and V. Golkov and G. E. Gürel and D. Cremers}, title = {Precursor {microRNA} Identification Using Deep Convolutional Neural Networks}, year = {2018}, % howpublished = {Preprint}, journal = {bioRxiv preprint 414656}, keywords = {precursor microRNA, pre-miRNA, miRNA, deep learning, neural networks, machine learning, biology, gene regulation}, } @article{Golkov-et-al-2020-ROC, author = {V. Golkov and A. Becker and D. T. Plop and D. Čuturilo and N. Davoudi and J. Mendenhall and R. Moretti and J. Meiler and D. Cremers}, title = {Deep Learning for Virtual Screening: Five Reasons to Use {ROC} Cost Functions}, year = {2020}, journal = {arXiv preprint arXiv:2007.07029}, eprint = {2007.07029}, eprinttype = {arXiv}, keywords = {deep learning, drug discovery, virtual screening, neural networks, machine learning, QSAR, classification, receiver operating characteristic, biology, chemistry}, } @article{Aljalbout-et-al-2018, author = {E. Aljalbout and V. Golkov and Y. Siddiqui and M. Strobel and D. Cremers}, title = {Clustering with Deep Learning: Taxonomy and New Methods}, year = {2018}, journal = {arXiv preprint arXiv:1801.07648}, eprint = {1801.07648}, eprinttype = {arXiv}, keywords = {deep learning, clustering, cluster analysis, neural networks}, } @inproceedings{haeusser18associative, author = {P. Haeusser and J. Plapp and V. Golkov and E. Aljalbout and D. Cremers}, title = {Associative Deep Clustering - Training a Classification Network with no Labels}, booktitle = {Proc. of the German Conference on Pattern Recognition (GCPR)}, year = {2018}, month = {October}, titleurl = {haeusser18associative.pdf}, keywords = {Clustering, Embeddings, deep learning, associative_learning}, } @article{mayer18synthetic, author = {N Mayer and E Ilg and P Fischer and C Hazirbas and D Cremers and A Dosovitskiy and T Brox}, title = {What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?}, booktitle = {International Journal of Computer Vision}, volume = {41}, number = {8}, pages = {1797--1812}, year = {2018}, month = {September}, eprint = {arXiv:1801.06397}, keywords = {deep learning, optical-flow}, } @inproceedings{Frerix-et-al-18, author = {T. Frerix and T. Möllenhoff and M. Moeller and D. Cremers}, title = {Proximal Backpropagation}, booktitle = {International Conference on Learning Representations (ICLR)}, primaryclass = {cs.LG}, year = {2018}, } @inproceedings{stumberg18vidso, author = {L. von Stumberg and V. Usenko and D. Cremers}, title = {Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization}, booktitle = {International Conference on Robotics and Automation (ICRA)}, year = {2018}, month = {May}, keywords = {dso, vi-dso, vslam}, } @inproceedings{schubert2018vidataset, author = {D. Schubert and T. Goll and N. Demmel and V. Usenko and J. Stueckler and D. Cremers}, title = {The TUM VI Benchmark for Evaluating Visual-Inertial Odometry}, booktitle = {International Conference on Intelligent Robots and Systems (IROS)}, arxiv = {arXiv:1804.06120}, year = {2018}, month = {October}, keywords = {tumvi, vo, vio ,vslam, dataset}, } @inproceedings{schubert2019vidsors, author = {D. Schubert and N. Demmel and L. von Stumberg and V. Usenko and D. Cremers}, title = {Rolling-Shutter Modelling for Visual-Inertial Odometry}, booktitle = {International Conference on Intelligent Robots and Systems (IROS)}, year = {2019}, month = {November}, arxiv = {arXiv: 1911.01015}, keywords = {vidsors, vo, vio ,vslam}, } @inproceedings{gao2018ldso, author = {X. Gao and R. Wang and N. Demmel and D. Cremers}, title = {LDSO: Direct Sparse Odometry with Loop Closure}, booktitle = {International Conference on Intelligent Robots and Systems (IROS)}, arxiv = {arXiv:1808.01111}, year = {2018}, month = {October}, keywords = {dso, ldso, vslam}, } @article{matsuki2018_omnidso, title = {Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras}, author = {H. Matsuki and L. von Stumberg and V. Usenko and J. Stueckler and D. Cremers}, journal = {IEEE Robotics and Automation Letters & Int. Conference on Intelligent Robots and Systems (IROS)}, booktitle = {Robotics and Automation Letters}, publisher = {IEEE}, year = {2018}, keywords = {dso, vslam}, } @inproceedings{eisenberger2019divfree, author = {M. Eisenberger and Z. Lähner and D. Cremers}, title = {Divergence-Free Shape Correspondence by Deformation}, booktitle = {Computer Graphics Forum}, year = {2019}, volume = {38}, number = {5}, pages = {1-12}, month = {July}, keywords = { Geometry Processing }, } @inproceedings{laehner2018deepwrinkles, author = {Z. Lähner and D. Cremers and T. Tung}, title = {DeepWrinkles: Accurate and Realistic Clothing Modeling}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2018}, month = {September}, award = {Oral Presentation}, keywords = { Geometry Processing }, } @inproceedings{yang2018dvso, author = {N. Yang and R. Wang and J. Stueckler and D. Cremers}, title = {Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2018}, month = {September}, award = {Oral Presentation}, keywords = {dso, dvso, deep learning, monocular depth estimation, semi-supervised learning, slam, visual odometry, vslam}, } @inproceedings{schubert2018drso, author = {D. Schubert and N. Demmel and V. Usenko and J. Stueckler and D. Cremers}, title = {Direct Sparse Odometry With Rolling Shutter}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2018}, month = {September}, award = {Oral Presentation}, keywords = {dso, vslam}, } @inproceedings{usenko18double-sphere, author = {V. Usenko and N. Demmel and D. Cremers}, title = {The Double Sphere Camera Model}, booktitle = {Proc. of the Int. Conference on 3D Vision (3DV)}, year = {2018}, month = {September}, eprint = {1807.08957}, eprinttype = {arXiv}, eprintclass = {cs.CV}, keywords = {double-sphere, vslam}, } @article{lingni18wcnn, author = {L. Ma and J. Stueckler and T. Wu and D. Cremers}, title = {Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform}, year = {2018}, month = {Aug}, booktitle = {arXiv:1808.01834}, arxiv = {arXiv:1808.01834}, } @article{laude2021lifting, title = {Lifting the Convex Conjugate in Lagrangian Relaxations: {A} Tractable Approach for Continuous Markov Random Fields}, author = {H Bauermeister and E Laude and T Möllenhoff and M Möller and D Cremers}, journal = {{SIAM} J. Imaging Sci.}, volume = {15}, number = {3}, pages = {1253--1281}, year = {2022}, keywords = {Markov random fields, moment relaxation, sum of squares, polynomial optimization, generalized conjugacy, optimal transport}, } @inproceedings{chiotellis2018ilp, author = {I. Chiotellis and F. Zimmermann and D. Cremers and R. Triebel}, title = {Incremental Semi-Supervised Learning from Streams for Object Classification}, booktitle = {International Conference on Intelligent Robots and Systems (IROS)}, address = {Madrid, Spain}, year = {2018}, month = {Oct.}, } @inproceedings{estellers2018, author = {V. Estellers and F. Schmidt and D. Cremers}, title = {Robust Fitting of Subdivision Surfaces for Smooth Shape Analysis}, booktitle = {Proc. of the Int. Conference on 3D Vision (3DV)}, year = {2018}, month = {September}, keywords = {Geometry Processing}, topic = {Shape Analysis}, award = {Received the Best Paper Award at 3DV 2018}, } @article{tjaden2019region, title = {A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking}, author = {H Tjaden and U Schwanecke and E Schömer and D Cremers}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {41}, number = {8}, pages = {1797--1812}, year = {2019}, titleurl = {tjaden_et_al_pami18.pdf}, } @inproceedings{wenzel18corl, author = {P. Wenzel and Q. Khan and D. Cremers and L. Leal-Taixé}, booktitle = {Conference on Robot Learning ({CoRL})}, title = {Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using {GANs}}, year = {2018}, keywords = {deep learning}, } @inproceedings{Frerix2020, author = {T Frerix and M Nießner and D Cremers}, title = {Homogeneous Linear Inequality Constraints for Neural Network Activations}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, year = {2020}, } @inproceedings{Frerix2021, title = {Variational Data Assimilation with a Learned Inverse Observation Operator}, author = {T Frerix and D Kochkov and J Smith and D Cremers and M Brenner and S Hoyer}, booktitle = {Proceedings of the 38th International Conference on Machine Learning (ICML)}, year = {2021}, } @article{roy2019noninvasive, title = {A Non-invasive {3D} Body Scanner and Software Tool towards Analysis of Scoliosis}, author = {S. Roy and A.T.D. Gruenwald and A. Alves-Pinto and R. Maier and D. Cremers and D. Pfeiffer and R. Lampe}, journal = {BioMed Research International (BMRI)}, year = {2019}, month = {May}, keywords = {rgb-d,reconstruction,3d-reconstruction,3d-scanning,medical,medical imaging}, } @inproceedings{laude-wu-cremers-aistats-19, author = {E. Laude and T. Wu and D. Cremers}, title = {Optimization of Inf-Convolution Regularized Nonconvex Composite Problems}, booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)}, year = {2019}, titleurl = {laude-wu-cremers-aistats-19.pdf}, } @inproceedings{moellenh-cvpr-19, author = {T. Möllenhoff and D. Cremers}, title = {Lifting Vectorial Variational Problems: A Natural Formulation based on Geometric Measure Theory and Discrete Exterior Calculus}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2019}, titleurl = {moellenh-cvpr-19.pdf}, award = {Oral Presentation}, } @article{usenko19nfr, author = {V. Usenko and N. Demmel and D. Schubert and J. Stueckler and D. Cremers}, title = {Visual-Inertial Mapping with Non-Linear Factor Recovery}, journal = {IEEE Robotics and Automation Letters (RA-L) & Int. Conference on Intelligent Robotics and Automation (ICRA)}, publisher = {IEEE}, year = {2020}, volume = {5}, number = {2}, pages = {422-429}, keywords = {nfr, vo, vio, vslam}, doi = {10.1109/LRA.2019.2961227}, } @article{Pasa-et-al-2019, author = {F. Pasa and V. Golkov and F. Pfeiffer and D. Cremers and D. Pfeiffer}, title = {Efficient Deep Network Architectures for Fast Chest {X}-Ray Tuberculosis Screening and Visualization}, journal = {Scientific Reports}, year = {2019}, volume = {9}, number = {1}, pages = {6268}, issn = {2045-2322}, doi = {10.1038/s41598-019-42557-4}, url = {https://www.nature.com/articles/s41598-019-42557-4}, keywords = {medical imaging, deep learning}, } @inproceedings{wang2020directshape, author = {R. Wang and N. Yang and J. Stueckler and D. Cremers}, title = {DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation}, booktitle = {Proc. of the IEEE International Conference on Robotics and Automation (ICRA)}, year = {2020}, keywords = {stereo, 3D reconstruction, semantic SLAM, 3D object detection, scene understanding, direct shape}, } @article{Schuchardt-et-al-2019, author = {J. Schuchardt and V. Golkov and D. Cremers}, title = {Learning to Evolve}, year = {2019}, journal = {arXiv preprint arXiv:1905.03389}, eprint = {1905.03389}, eprinttype = {arXiv}, keywords = {evolutionary algorithms, evolutionary computation, genetic algorithms, deep learning, neural networks, reinforcement learning}, } @inproceedings{flatgan-icml-19, author = {T. Möllenhoff and D. Cremers}, title = {Flat Metric Minimization with Applications in Generative Modeling}, booktitle = {International Conference on Machine Learning (ICML)}, primaryclass = {cs.LG}, year = {2019}, award = {Full Oral Presentation}, month = {6}, } @article{gn-net-2020, author = {L. von Stumberg and P. Wenzel and Q. Khan and D. Cremers}, title = {{GN-Net}: The Gauss-Newton Loss for Multi-Weather Relocalization}, journal = {{IEEE} Robotics and Automation Letters ({RA-L})}, year = {2020}, volume = {5}, number = {2}, pages = {890-897}, keywords = {gn-net, vslam, deep learning}, } @article{sommer20planes, author = {C. Sommer and Y. Sun and L. J. Guibas and D. Cremers and T. Birdal}, title = {From Planes to Corners: Multi-Purpose Primitive Detection in Unorganized 3D Point Clouds}, journal = {IEEE Robotics and Automation Letters (RA-L) & International Conference on Robotics and Automation (ICRA)}, year = {2020}, volume = {5}, number = {2}, pages = {1764-1771}, eprint = {2001.07360}, eprinttype = {arXiv}, eprintclass = {cs.CV}, doi = {10.1109/LRA.2020.2969936}, keywords = {Geometry Processing}, } @inproceedings{eisenberger2019smoothshells, author = {M. Eisenberger and Z. Lähner and D. Cremers}, title = {Smooth Shells: Multi-Scale Shape Registration with Functional Maps}, booktitle = {IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2020}, award = {Oral Presentation}, keywords = { Geometry Processing }, } @inproceedings{eisenberger2020hamiltonian, author = {M. Eisenberger and D. Cremers}, title = {Hamiltonian Dynamics for Real-World Shape Interpolation}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2020}, award = {Spotlight Presentation}, keywords = {Shape Analysis, Geometry Processing}, } @inproceedings{eisenberger2020deepshells, author = {M. Eisenberger and A. Toker and L. Leal-Taixé and D. Cremers}, title = {Deep Shells: Unsupervised Shape Correspondence with Optimal Transport}, booktitle = {34th Conference on Neural Information Processing Systems (NeurIPS)}, year = {2020}, } @inproceedings{eisenberger2021neuromorph, author = {M. Eisenberger and D. Novotny and G. Kerchenbaum and P. Labatut and N. Neverova and D. Cremers and A. Vedaldi}, title = {NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go}, booktitle = {IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2021}, } @inproceedings{eisenberger2022unified, author = {M. Eisenberger and A. Toker and L. Leal-Taixé and F. Bernard and D. Cremers}, title = {A Unified Framework for Implicit Sinkhorn Differentiation}, booktitle = {IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2022}, } @inproceedings{eisenberger2023gmsm, author = {M. Eisenberger and A. Toker and L. Leal-Taixé and D. Cremers}, title = {G-MSM: Unsupervised Multi-Shape Matching with Graph-based Affinity Priors}, booktitle = {IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2023}, } @inproceedings{mukkamala2021bregman, address = {Cham}, author = {M. C. Mukkamala and F. Westerkamp and E. Laude and D. Cremers and P. Ochs}, booktitle = {Scale Space and Variational Methods in Computer Vision}, date-modified = {2021-05-18 17:23:47 +0200}, editor = {Elmoataz, Abderrahim and Fadili, Jalal and Qu{\'e}au, Yvain and Rabin, Julien and Simon, Lo{\"\i}c}, isbn = {978-3-030-75549-2}, pages = {204--215}, publisher = {Springer International Publishing}, title = {Bregman Proximal Gradient Algorithms for Deep Matrix Factorization}, year = {2021}, eprint = {1910.03638}, eprinttype = {arXiv}, eprintclass = {math.OC}, } @inproceedings{Weiss-et-al-cvpr2020, author = {S. Weiss and R. Maier and D. Cremers and R. Westermann and N. Thuerey}, title = {Correspondence-Free Material Reconstruction using Sparse Surface Constraints}, booktitle = {IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2020}, titleurl = {Weiss_et_al_cvpr2020.pdf}, keywords = {Geometry Processing}, } @inproceedings{control-across-weathers-19, author = {Q. Khan and P. Wenzel and D. Cremers and L. Leal-Taixé}, title = {Towards Generalizing Sensorimotor Control Across Weather Conditions}, booktitle = {Proceedings of the {IEEE/RSJ} International Conference on Intelligent Robots and Systems ({IROS})}, year = {2019}, keywords = {deep learning}, } @inproceedings{moeller-et-al-19, author = {M. Moeller and T. Möllenhoff and D. Cremers}, title = {Controlling Neural Networks via Energy Dissipation}, booktitle = {International Conference on Computer Vision (ICCV)}, year = {2019}, month = {10}, address = {Seoul, South Korea}, eprint = {1904.03081}, eprinttype = {arXiv}, eprintclass = {cs.CV}, } @inproceedings{jung2019corl, author = {E. Jung and N. Yang and D. Cremers}, booktitle = {Conference on Robot Learning (CoRL)}, title = {{Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light}}, award = {Full Oral Presentation}, year = {2019}, } @inproceedings{weiss2019sparse, title = {Sparse Surface Constraints for Combining Physics-based Elasticity Simulation and Correspondence-Free Object Reconstruction}, author = {S. Weiss and R. Maier and R. Westermann and D. Cremers and N. Thuerey}, journal = {preprint}, booktitle = {arXiv preprint arXiv:1910.01812}, year = {2019}, eprint = {1910.01812}, eprinttype = {arXiv}, eprintclass = {cs.CV}, keywords = {Geometry Processing}, } @article{Della-Libera-et-al-2019, author = {L. Della Libera and V. Golkov and Y. Zhu and A. Mielke and D. Cremers}, title = {Deep Learning for {2D and 3D} Rotatable Data: An Overview of Methods}, year = {2019}, journal = {arXiv preprint arXiv:1910.14594}, eprint = {1910.14594}, eprinttype = {arXiv}, keywords = {deep learning, neural networks, 2D, 3D, rotations, invariance, equivariance}, } @inbook{vi-dso-chapter, author = {L. von Stumberg and V. Usenko and D. Cremers}, title = {A Review and Quantitative Evaluation of Direct Visual–Inertial Odometry}, editor = {M. Yang and B. Rosenhahn and V. Murino}, chapter = {Multimodal Scene Understanding}, publisher = {Academic Press}, pages = {159--198}, year = {2019}, doi = {10.1016/B978-0-12-817358-9.00013-5}, isbn = {978-0-12-817358-9}, keywords = {vo, vio, vslam}, } @inbook{usenko2020_tumflyers, author = {V. Usenko and L. von Stumberg and J. Stückler and D. Cremers}, editor = {F. Caccavale and C. Ott and B. Winkler and Z. Taylor}, title = {TUM Flyers: Vision---Based MAV Navigation for Systematic Inspection of Structures}, chapter = {Bringing Innovative Robotic Technologies from Research Labs to Industrial End-users}, year = {2020}, publisher = {Springer International Publishing}, address = {Cham}, pages = {189--209}, isbn = {978-3-030-34507-5}, doi = {10.1007/978-3-030-34507-5_8}, keywords = {vo, vio, vslam}, } @inproceedings{sommer19spline, author = {C. Sommer and V. Usenko and D. Schubert and N. Demmel and D. Cremers}, title = {Efficient Derivative Computation for Cumulative B-Splines on Lie Groups}, eprint = {1911.08860}, eprinttype = {arXiv}, eprintclass = {cs.CV}, doi = {10.1109/CVPR42600.2020.01116}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2020}, award = {Oral Presentation}, keywords = {lie-spline, vslam, SLAM}, } @inproceedings{brechet2019, title = {Informative GANs via Structured Regularization of Optimal Transport}, author = {P. Bréchet and T. Wu and T. Möllenhoff and D. Cremers}, booktitle = {{NeurIPS Workshop on Optimal Transport and Machine Learning}}, year = {2019}, eprint = {1912.02160}, eprinttype = {arXiv}, eprintclass = {cs.CV}, } @inproceedings{yang20d3vo, author = {N. Yang and L. von Stumberg and R. Wang and D. Cremers}, title = {D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2020}, eprint = {2003.01060}, eprinttype = {arXiv}, eprintclass = {cs.CV}, award = {Oral Presentation}, keywords = {dso,dvso, deep learning, monocular depth estimation, semi-supervised learning, slam, visual odometry,d3vo, vslam}, } @inproceedings{ye2020optimization, author = {Z. Ye and T. Möllenhoff and T. Wu and D. Cremers}, title = {Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning}, booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)}, year = {2020}, titleurl = {ye-et-al-combinatorial-20.pdf}, } @inproceedings{ye2021gcpr, author = {Z. Ye and B. Haefner and Y. Quéau and T. Möllenhoff and D. Cremers}, title = {Sublabel-Accurate Multilabeling Meets Product Label Spaces}, booktitle = {DAGM German Conference on Pattern Recognition (GCPR)}, year = {2021}, doi = {10.1007/978-3-030-92659-5_1}, %eprint = {}, %eprinttype = {arXiv}, %eprintclass = {cs.CV}, award = {Oral Presentation}, %titleurl = {}, keywords = {}, } @article{ye2022ijcv, author = {Z. Ye and B. Haefner and Y. Quéau and T. Möllenhoff and D. Cremers}, title = {A Cutting-Plane Method for Sublabel-Accurate Relaxation of Problems with Product Label Spaces}, journal = {International Journal of Computer Vision (IJCV)}, year = {2022}, doi = {10.1007/s11263-022-01704-7}, titleurl = {ye2022ijcv.pdf}, keywords = {}, } @inbook{Moeller2018, author = {M Moeller and D Cremers}, editor = {Bertalm{\'i}o, Marcelo}, title = {Image Denoising --- Old and New}, booktitle = {Denoising of Photographic Images and Video: Fundamentals, Open Challenges and New Trends}, year = {2018}, publisher = {Springer International Publishing}, address = {Cham}, pages = {63--91}, titleurl = {moeller_cremers2020_denoising_old_and_new.pdf}, } @inproceedings{liu2020effective, title = {Effective Version Space Reduction for Convolutional Neural Networks}, author = {J Liu and I Chiotellis and R Triebel and D Cremers}, journal = {arXiv preprint arXiv:2006.12456}, year = {2020}, booktitle = {European Conference on Machine Learning and Data Mining (ECML-PKDD)}, keywords = {deep learning, active learning, convolutional neural networks}, } @inproceedings{du2020dh3d, author = {J. Du and R. Wang and D. Cremers}, title = {DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2020}, award = {Spotlight Presentation}, keywords = {SLAM, localization, re-localization, 3D learning, dh3d, vslam}, } @inproceedings{sommer20primitect, author = {C. Sommer and Y. Sun and E. Bylow and D. Cremers}, title = {PrimiTect: Fast Continuous Hough Voting for Primitive Detection}, booktitle = {International Conference on Robotics and Automation (ICRA)}, eprint = {2005.07457}, eprinttype = {arXiv}, eprintclass = {cs.CV}, doi = {10.1109/ICRA40945.2020.9196988}, year = {2020}, keywords = {Geometry Processing}, } @inproceedings{koestler2020learning, author = {L. Koestler and N. Yang and R. Wang and D. Cremers}, title = {Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels}, booktitle = {Proceedings of the German Conference on Pattern Recognition (GCPR)}, year = {2020}, } @inproceedings{wenzel2020fourseasons, title = {{4Seasons}: A Cross-Season Dataset for Multi-Weather {SLAM} in Autonomous Driving}, author = {P. Wenzel and R. Wang and N. Yang and Q. Cheng and Q. Khan and L. von Stumberg and N. Zeller and D. Cremers}, booktitle = {Proceedings of the German Conference on Pattern Recognition ({GCPR})}, year = {2020}, keywords = {vslam,4seasons, deep learning}, } @inproceedings{holzschuh20simanneal, author = {B Holzschuh and Z Lähner and D Cremers}, title = {Simulated Annealing for 3D Shape Correspondence}, booktitle = {International Conference on 3D Vision (3DV)}, year = {2020}, award = {Oral Presentation}, keywords = {Shape Analysis, Shape Matching, Geometry Processing}, } @inproceedings{aygun20heatkernel, author = {M Aygün and Z Lähner and D Cremers}, title = {Unsupervised Dense Shape Correspondence using Heat Kernels}, booktitle = {International Conference on 3D Vision (3DV)}, year = {2020}, keywords = {Shape Analysis, Shape Matching, Geometry Processing}, } @article{Naeyaert2020, author = {M. Naeyaert and J. Aelterman and J. Van Audekerke and V. Golkov and D. Cremers and A. Pižurica and J. Sijbers and M. Verhoye}, title = {Accelerating in vivo fast spin echo high angular resolution diffusion imaging with an isotropic resolution in mice through compressed sensing}, journal = {Magnetic Resonance in Medicine}, year = {2020}, volume = {85}, number = {3}, pages = {1397-1413}, keywords = {compressed sensing, diffusion, fast spin echo, HARDI, turbo spin echo, medical imaging, diffusion MRI}, doi = {10.1002/mrm.28520}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.28520}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.28520}, } @inproceedings{demmel2020distributed, author = {N Demmel and M Gao and E Laude and T Wu and D Cremers}, title = {Distributed Photometric Bundle Adjustment}, booktitle = {International Conference on 3D Vision (3DV)}, year = {2020}, award = {Oral Presentation}, keywords = {photometric-bundle-adjustment, slam, structure-from-motion, direct-method, distributed-optimization, mapping, splitting-method, penalty-method, loop-closure, odometry, consensus-optimization, dpba, vslam}, } @article{fabbro2020, title = {Speech Synthesis and Control Using Differentiable {DSP}}, author = {G Fabbro and V Golkov and T Kemp and D Cremers}, year = {2020}, journal = {arXiv preprint arXiv:2010.15084}, eprint = {2010.15084}, eprinttype = {arXiv}, primaryclass = {eess.AS}, keywords = {speech synthesis, neural vocoder, text-to-speech, digital signal processing, neural networks, deep learning}, } @inproceedings{lm-reloc-2020, author = {L. von Stumberg and P. Wenzel and N. Yang and D. Cremers}, title = {LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization}, booktitle = {International Conference on 3D Vision (3DV)}, year = {2020}, keywords = {lm-reloc, slam, structure-from-motion, direct method, mapping, vslam, deep learning}, } @inproceedings{wimbauer2020monorec, title = {MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera}, author = {F. Wimbauer and N. Yang and L. von Stumberg and N. Zeller and D Cremers}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2021}, eprint = {2011.11814}, eprinttype = {arXiv}, eprintclass = {cs.CV}, keywords = {monorec, dvso, d3vo, mvs, deep learning, SLAM, vslam, reconstruction}, } @inproceedings{yenamandra2020i3dmm, author = {T Yenamandra and A Tewari and F Bernard and HP Seidel and M Elgharib and D Cremers and C Theobalt}, title = {i3DMM: Deep Implicit 3D Morphable Model of Human Heads}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, keywords = {Geometry Processing, implicit representations, correspondences, face model}, award = {Oral Presentation}, } @article{chiotellis2020noge, title = {Neural Online Graph Exploration}, author = {I Chiotellis and D Cremers}, year = {2020}, journal = {arXiv preprint arXiv:2012.03345}, eprint = {2012.03345}, archiveprefix = {arXiv}, primaryclass = {cs.LG}, keywords = {deep learning, exploration, graph neural networks, learning on graphs, artificial intelligence}, } @inproceedings{gao2021multi, title = {Isometric Multi-Shape Matching}, author = {M Gao and Z Lähner and J Thunberg and D Cremers and F Bernard}, year = {2021}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, keywords = {Shape Analysis, Geometry Processing, Shape Correspondence, Multi Shape Matching}, award = {Oral Presentation}, } @article{mueller2021, title = {Rotation-Equivariant Deep Learning for Diffusion {MRI}}, author = {P. Müller and V. Golkov and V. Tomassini and D. Cremers}, year = {2021}, journal = {arXiv preprint}, eprint = {2102.06942}, eprinttype = {arXiv}, primaryclass = {cs.CV}, keywords = {deep learning, diffusion MRI, equivariant deep learning, rotation-equivariance, magnetic resonance imaging, multiple sclerosis, image segmentation, medical imaging}, } @inproceedings{Naeyaert2021, author = {M Naeyaert and V Golkov and D Cremers and J Sijbers and M Verhoye}, title = {Faster and better {HARDI} using {FSE} and holistic reconstruction}, year = {2021}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, keywords = {compressed sensing, magnetic resonance imaging, diffusion MRI, fast spin echo, HARDI, turbo spin echo, primal-dual, space of positions and orientations, medical imaging, image reconstruction, inverse problems}, } @inproceedings{Mueller2021-ISMRM, title = {Rotation-Equivariant Deep Learning for Diffusion {MRI} (short version)}, author = {P. Müller and V. Golkov and V. Tomassini and D. Cremers}, year = {2021}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, keywords = {deep learning, diffusion MRI, equivariant deep learning, rotation-equivariance, magnetic resonance imaging, multiple sclerosis, image segmentation, medical imaging}, } @inproceedings{selfcontrol-aistats-2021, author = {Q. Khan and P. Wenzel and D. Cremers}, title = {Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry}, booktitle = {International Conference on Artificial Intelligence and Statistics ({AISTATS})}, year = {2021}, keywords = {deep learning}, } @inproceedings{gladkova2021tight, author = {M Gladkova and R Wang and N Zeller and D Cremers}, title = {Tight Integration of Feature-based Relocalization in Monocular Direct Visual Odometry}, booktitle = {Proc. of the IEEE International Conference on Robotics and Automation (ICRA)}, year = {2021}, eprint = {2102.01191}, eprinttype = {arXiv}, eprintclass = {cs.CV}, keywords = {relocalization, localization, dso, ldso, tirdso, vslam}, } @inproceedings{yan2021soe, author = {Y. Xia and Y. Xu and S. Li and R. Wang and J. Du and D. Cremers and U. Stilla}, title = {SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2021}, award = {Oral Presentation}, keywords = {SLAM, localization, re-localization, 3D learning, dh3d, vslam, deep learning}, } @inproceedings{wenzel2021icra, author = {P. Wenzel and T. Schön and L. Leal-Taixé and D. Cremers}, title = {Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning}, keywords = {deep learning}, booktitle = {Proceedings of the {IEEE} International Conference on Robotics and Automation ({ICRA})}, year = {2021}, } @inproceedings{demmel2021rootba, author = {N Demmel and C Sommer and D Cremers and V Usenko}, title = {Square Root Bundle Adjustment for Large-Scale Reconstruction}, eprint = {2103.01843}, eprinttype = {arXiv}, eprintclass = {cs.CV}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2021}, keywords = {bundle adjustment, structure from motion, optimization, square root estimation, rootba, SLAM, vslam}, } @inproceedings{tomani2021posthoc, author = {C Tomani and S Gruber and ME Erdem and D Cremers and F Buettner}, title = {Post-hoc Uncertainty Calibration for Domain Drift Scenarios}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2021}, award = {Oral Presentation}, eprint = {2012.10988}, eprinttype = {arXiv}, keywords = {deep learning}, } @inproceedings{tomani2021pts, title = {Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration}, author = {C Tomani and D Cremers and F Buettner}, year = {2022}, booktitle = {European Conference on Computer Vision (ECCV)}, eprint = {2102.12182}, eprinttype = {arXiv}, keywords = {deep learning}, } @article{tomani2022challenger, title = {Challenger: Training with Attribution Maps}, author = {C Tomani and D Cremers}, year = {2022}, journal = {arXiv preprint}, eprint = {2205.15094}, eprinttype = {arXiv}, primaryclass = {cs.LG}, keywords = {deep learning}, } @inproceedings{tomani2023dac, title = {Beyond In-Domain Scenarios: Robust Density-Aware Calibration}, author = {C Tomani and F Waseda and Y Shen and D Cremers}, booktitle = {Proceedings of the 40th International Conference on Machine Learning (ICML)}, year = {2023}, eprint = {2302.05118}, eprinttype = {arXiv}, keywords = {deep learning}, } @article{tomani2023qualityaware, title = {Quality Control at Your Fingertips: Quality-Aware Translation Models}, author = {C Tomani and D Vilar and M Freitag and C Cherry and S Naskar and M Finkelstein and D Cremers}, year = {2023}, journal = {arXiv preprint}, eprint = {2310.06707}, eprinttype = {arXiv}, primaryclass = {cs.LG}, keywords = {deep learning}, } @inproceedings{Publ2015-866, author = {J. Duran and M. Moeller and C. Sbert and D. Cremers}, title = {A Novel Framework for Nonlocal Vectorial Total Variation Based on $\ell^{p,q,r}${\^a}ˆ’norms}, booktitle = {Proceedings of the 10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition}, year = {2015}, pages = {141-154}, publisher = {Springer International Publishing}, } @inproceedings{demmel2021rootvo, author = {N Demmel and D Schubert and C Sommer and D Cremers and V Usenko}, title = {Square Root Marginalization for Sliding-Window Bundle Adjustment}, eprint = {2109.02182}, eprinttype = {arXiv}, eprintclass = {cs.CV}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2021}, keywords = {odometry, VO, VIO, visual-inertial, bundle adjustment, optimization, square root estimation, rootba, rootvo, SLAM, vslam}, } @inproceedings{wudenka2021monocular, author = {MW Wudenka and MG Müller and N Demmel and A Wedler and R Triebel and D Cremers and W Stuerzl}, title = {Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions}, eprint = {2109.05509}, eprinttype = {arXiv}, eprintclass = {cs.RO}, booktitle = {International Conference on Intelligent Robots and Systems (IROS)}, keywords = {odometry, VO, SLAM, vslam}, year = {2021}, } @inproceedings{klenk2021tumvie, author = {S Klenk and J Chui and N Demmel and D Cremers}, title = {TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset}, eprint = {2108.07329}, eprinttype = {arXiv}, eprintclass = {cs.CV}, booktitle = {International Conference on Intelligent Robots and Systems (IROS)}, year = {2021}, keywords = {tumvie, event camera, dynamic vision sensor, SLAM, vslam}, } @article{chui2021etslo, title = {Event-Based Feature Tracking in Continuous Time with Sliding Window Optimization}, author = {J. Chui and S. Klenk and D. Cremers}, year = {2021}, journal = {arXiv preprint}, eprint = {2107.04536}, eprinttype = {arXiv}, primaryclass = {cs.CV}, keywords = {Dynamic vision sensor, Continuous-time feature tracking, Sliding window, B-splines, SE2 warping, SLAM, vslam}, } @inproceedings{koestler2021tandem, author = {L Koestler and N Yang and N Zeller and D Cremers}, title = {TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo}, booktitle = {Conference on Robot Learning (CoRL)}, year = {2021}, eprint = {2111.07418}, eprinttype = {arXiv}, award = {3DV'21 Best Demo Award}, keywords = {tandem, odometry, VO, SLAM, vslam, dense reconstruction, mvs}, } @inproceedings{weber2021mcg, author = {S Weber and N Demmel and D Cremers}, title = {Multidirectional Conjugate Gradients for Scalable Bundle Adjustment}, booktitle = {German Conference on Pattern Recognition (GCPR)}, year = {2021}, keywords = {large-scale reconstruction, bundle adjustment, preconditioned conjugate gradients, SLAM, vslam}, award = {Oral Presentation}, } @article{mozes2021, title = {Scene Graph Generation for Better Image Captioning?}, author = {M. Mozes and M. Schmitt and V. Golkov and H. Schütze and D. Cremers}, year = {2021}, journal = {arXiv preprint}, eprint = {2109.11398}, eprinttype = {arXiv}, primaryclass = {cs.CV}, keywords = {deep learning, computer vision, natural language processing, image captioning, scene graphs, attention mechanism}, } @article{stumberg22dmvio, author = {L. von Stumberg and D. Cremers}, title = {{DM-VIO}: Delayed Marginalization Visual-Inertial Odometry}, journal = {{IEEE} Robotics and Automation Letters ({RA-L}) & International Conference on Robotics and Automation ({ICRA})}, year = {2022}, volume = {7}, number = {2}, pages = {1408-1415}, doi = {10.1109/LRA.2021.3140129}, keywords = {dm-vio, dso, vslam, SLAM, VIO, visual-inertial, vi-dso}, } @inproceedings{Sommer2022, author = {C Sommer and L Sang and D Schubert and D Cremers}, title = {Gradient-{SDF}: {A} Semi-Implicit Surface Representation for 3D Reconstruction}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2022}, url = {https://arxiv.org/abs/2111.13652}, titleurl = {sommer2022.png}, } @inproceedings{ye2021joint, title = {Joint Deep Multi-Graph Matching and {3D} Geometry Learning from Inhomogeneous {2D} Image Collections}, author = {Z Ye and T Yenamandra and F Bernard and D Cremers}, booktitle = {AAAI}, titleurl = {ye2021joint.pdf}, year = {2022}, } @article{Brunner2022, author = {C. Brunner and A. Duensing and C. Schröder and M. Mittermair and V. Golkov and M. Pollanka and D. Cremers and R. Kienberger}, title = {Deep Learning in Attosecond Metrology}, journal = {Optics Express}, year = {2022}, keywords = {attosecond metrology, photoelectron spectroscopy, deep learning, physics, attosecond streak camera, streaking, Neural networks; Phase retrieval; Photoelectron spectra; Power spectra; Streak cameras; Time resolved spectroscopy}, volume = {30}, number = {9}, pages = {15669--15684}, publisher = {OSA}, url = {https://opg.optica.org/OE/abstract.cfm?uri=OE-30-9-15669}, doi = {10.1364/OE.452108}, award = {Editor's Pick}, } @article{yenamandra2022fire, url = {https://arxiv.org/abs/2203.16284}, author = {T Yenamandra and A Tewari and N Yang and F Bernard and C Theobalt and D Cremers}, title = {FIRe🔥: Fast Inverse Rendering using Directional and Signed Distance Functions}, publisher = {arXiv}, year = {2022}, } @inproceedings{muhle2022pnec, author = {D Muhle and L Koestler and N Demmel and F Bernard and D Cremers}, title = {The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions}, eprint = {2204.02256}, eprinttype = {arXiv}, eprintclass = {cs.CV}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2022}, keywords = {pnec, vo, vslam}, } @inproceedings{pccontrol_2022, title = {Lateral Ego-Vehicle Control Without Supervision Using Point Clouds}, author = {F Müller and Q Khan and D Cremers}, booktitle = {Pattern Recognition and Artificial Intelligence}, publisher = {Springer International Publishing}, keywords = {deep learning}, pages = {477--488}, isbn = {978-3-031-09037-0}, year = {2022}, } @inproceedings{pathfinding_2022, title = {Biologically Inspired Neural Path Finding}, author = {L Hang and Q Khan and V Tresp and D Cremers}, booktitle = {Brain Informatics}, publisher = {Springer International Publishing}, keywords = {deep learning}, year = {2022}, } @inproceedings{ventriloquist_2022, title = {Ventriloquist-Net: Leveraging Speech Cues for Emotive Talking Head Generation}, author = {D Das and Q Khan and D Cremers}, booktitle = {IEEE International Conference on Image Processing}, keywords = {deep learning}, year = {2022}, } @inproceedings{koestler2022intrinsic, author = {L Koestler and D Grittner and M Moeller and D Cremers and Z Lähner}, title = {Intrinsic Neural Fields: Learning Functions on Manifolds}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2022}, eprint = {2203.07967}, eprinttype = {arXiv}, keywords = {Neural Fields, Shape Analysis, Geometry Processing}, } @inproceedings{gladkova2022directtracker, author = {M Gladkova and N Korobov and N Demmel and A Ošep and L Leal-Taixé and D Cremers}, title = {DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment}, booktitle = {International Conference on Intelligent Robots and Systems (IROS)}, year = {2022}, eprint = {2209.14965}, eprinttype = {arXiv}, eprintclass = {cs.CV}, keywords = {multi-object tracking, 3D object detection, slam, scene understanding}, } @inproceedings{hofherr2023neuralPhysParam, author = {F Hofherr and L Koestler and F Bernard and D Cremers}, title = {Neural Implicit Representations for Physical Parameter Inference from a Single Video}, booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, year = {2023}, eprint = {2204.14030}, eprinttype = {arXiv}, keywords = {Neural Fields, Physical Parameter Estimation, Geometry Processing}, } @inproceedings{wang2021epfgnn, author = {Y Wang and Y Shen and D Cremers}, title = {Explicit pairwise factorized graph neural network for semi-supervised node classification}, booktitle = {UAI}, year = {2021}, eprint = {2107.13059}, eprinttype = {arXiv}, eprintclass = {cs.LG}, keywords = {deep learning, graph neural network}, } @inproceedings{hsu2022gats, title = {What Makes Graph Neural Networks Miscalibrated?}, author = {HHH Hsu and Y Shen and C Tomani and D Cremers}, booktitle = {NeurIPS}, year = {2022}, eprint = {2210.06391}, eprinttype = {arXiv}, eprintclass = {cs.LG}, keywords = {deep learning, graph neural network, calibration}, } @inproceedings{shen2022dca, title = {Deep Combinatorial Aggregation}, author = {Y Shen and D Cremers}, booktitle = {NeurIPS}, year = {2022}, eprint = {2210.06436}, eprinttype = {arXiv}, eprintclass = {cs.LG}, keywords = {deep learning, uncertainty-aware learning}, } @inproceedings{sang2023high, author = {L Sang and B Haefner and X Zuo and D Cremers}, title = {High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF}, booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, address = {Hawaii, USA}, year = {2023}, eprint = {2210.12202}, eprinttype = {arXiv}, eprintclass = {cs.CV}, copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International}, titleurl = {sang2023high.png}, award = {Spotlight Presentation}, keywords = {3d-reconstruction,rgb-d,photometry}, } @inproceedings{hsu2022a, title = {A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs}, author = {HHH Hsu and Y Shen and D Cremers}, booktitle = {NeurIPS 2022 Workshop: New Frontiers in Graph Learning}, year = {2022}, eprint = {2210.15575}, eprinttype = {arXiv}, eprintclass = {cs.LG}, keywords = {deep learning, graph neural network, uncertainty estimation, calibration}, } @article{saroha2022implicit, title = {Implicit Shape Completion via Adversarial Shape Priors}, author = {A Saroha and M Eisenberger and T Yenamandra and D Cremers}, journal = {arXiv preprint arXiv:2204.10060}, year = {2022}, titleurl = {saroha2022implicit.png}, } @inproceedings{ehm2021shortest, title = {Shortest Paths in Graphs with Matrix-Valued Edges: Concepts, Algorithm and Application to 3D Multi-Shape Analysis}, author = {V Ehm and D Cremers and F Bernard}, booktitle = {2021 International Conference on 3D Vision (3DV)}, pages = {1186--1195}, year = {2021}, titleurl = {ehm2021_mvsp.png}, organization = {IEEE}, } @article{klenk2023nerf, title = {E-nerf: Neural radiance fields from a moving event camera}, author = {S Klenk and L Koestler and D Scaramuzza and D Cremers}, journal = {IEEE Robotics and Automation Letters}, volume = {8}, number = {3}, pages = {1587--1594}, year = {2023}, publisher = {IEEE}, keywords = {event camera, enerf, nerf}, } @inproceedings{klenk2022masked, title = {Masked Event Modeling: Self-Supervised Pretraining for Event Cameras}, author = {S Klenk and D Bonello and L Koestler and N Araslanov and D Cremers}, journal = {arXiv preprint arXiv:2212.10368}, year = {2024}, eprint = {2212.10368}, eprinttype = {arXiv}, eprintclass = {cs.CV}, booktitle = {{IEEE Winter Conference on Applications of Computer Vision (WACV)}}, month = {January}, address = {Hawaii, USA}, } @article{wenzel2022seasons, title = {4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions}, author = {P Wenzel and N Yang and R Wang and N Zeller and D Cremers}, journal = {arXiv preprint arXiv:2301.01147}, year = {2022}, eprint = {2301.01147}, eprinttype = {arXiv}, eprintclass = {cs.CV}, } @article{nndriving2023, title = {Learning vision based autonomous lateral vehicle control without supervision}, author = {Q Khan and I Sülö and M Öcal and D Cremers}, journal = {Applied Intelligence}, pages = {1--13}, year = {2023}, publisher = {Springer}, keywords = {intelligent driving, neural networks, deep learning}, } @inproceedings{sdrRobustTriangulation, author = {L Härenstam-Nielsen and N Zeller and D Cremers}, title = {Semidefinite Relaxations for Robust Multiview Triangulation}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2023}, titleurl = {robust_triangulation.png}, eprinttype = {arXiv}, eprint = {2301.11431}, eprintclass = {cs.CV}, } @inproceedings{weber2022psc, title = {Power Bundle Adjustment for Large-Scale 3D Reconstruction}, author = {S Weber and N Demmel and T Chon Chan and D Cremers}, eprint = {2204.12834}, eprinttype = {arXiv}, eprintclass = {cs.CV}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, keywords = {bundle adjustment, optimization, SLAM, vslam}, titleurl = {weber2022psc.pdf}, year = {2023}, } @inproceedings{wimbauer2023behind, title = {Behind the Scenes: Density Fields for Single View Reconstruction}, author = {F Wimbauer and N Yang and C Rupprecht and D Cremers}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2023}, eprinttype = {arXiv}, eprint = {2301.07668}, eprintclass = {cs.CV}, keywords = {depth prediction, volumetric, nerf, mvs, deep learning, SLAM, vslam, reconstruction}, titleurl = {wimbauer2023behind.png}, } @article{Wimmer2023, title = {Scale-Equivariant Deep Learning for 3D Data}, author = {T Wimmer and V Golkov and HN Dang and M Zaiss and A Maier and D Cremers}, year = {2023}, journal = {arXiv preprint}, eprint = {2304.05864}, eprinttype = {arXiv}, primaryclass = {cs.CV}, keywords = {deep learning, equivariant deep learning, scale-equivariance, magnetic resonance imaging, image segmentation, medical imaging}, } @inproceedings{weber2021step, title = {STEP: Segmenting and Tracking Every Pixel}, author = {M Weber and J Xie and M Collins and Y Zhu and P Voigtlaender and H Adam and B Green and A Geiger and B Leibe and D Cremers and others}, booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Track on Datasets and Benchmarks)}, volume = {1}, year = {2021}, } @article{weber2021deeplab2, title = {DeepLab2: A TensorFlow Library for Deep Labeling}, author = {M Weber and H Wang and S Qiao and J Xie and MD Collins and Y Zhu and L Yuan and D Kim and Q Yu and D Cremers and others}, journal = {arXiv preprint arXiv:2106.09748}, year = {2021}, } @article{dang2023, title = {Joint {MR} sequence optimization beats pure neural network approaches for spin-echo {MRI} super-resolution}, author = {HN Dang and V Golkov and T Wimmer and D Cremers and A Maier and M Zaiss}, journal = {arXiv preprint arXiv:2305.07524}, year = {2023}, keywords = {medical imaging, magnetic resonance imaging, pulse sequences, super-resolution, deep learning}, eprint = {2305.07524}, eprinttype = {arXiv}, } @inproceedings{zaiss2023, title = {{GPT4MR}: Exploring {GPT-4} as an {MR} Sequence and Reconstruction Programming Assistant}, author = {M Zaiss and HN Dang and V Golkov and J Rajput and D Cremers and F Knoll and A Maier}, year = {2023}, keywords = {medical imaging, magnetic resonance imaging, pulse sequences, deep learning, large language models, prompt engineering}, url = {https://docs.google.com/document/d/1iy6AaTWCpGjfVc5Z0ar7VXWyJyNq3PlY2NYoeKCD0T4/}, booktitle = {European Society for Magnetic Resonance in Medicine and Biology ({ESMRMB}) Annual Meeting}, award = {Oral Presentation}, } @inproceedings{ehm2023non, booktitle = {Eurographics 2023 - Posters}, title = {{Non-Separable Multi-Dimensional Network Flows for Visual Computing}}, author = {V Ehm and D Cremers and F Bernard}, year = {2023}, publisher = {The Eurographics Association}, issn = {1017-4656}, isbn = {978-3-03868-211-0}, titleurl = {ehm_vector_high_res.png}, doi = {10.2312/egp.20231028}, } @article{sang2023weight, title = {Weight-Aware Implicit Geometry Reconstruction with Curvature-Guided Sampling}, author = {L Sang and A Saroha and M Gao and D Cremers}, journal = {arXiv preprint arXiv:2306.02099}, year = {2023}, titleurl = {sang2023.png}, } @inproceedings{muhle2023learning, title = {Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares}, author = {D Muhle and L Koestler and KM Jatavallabhula and D Cremers}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, eprint = {2305.09527}, eprinttype = {arXiv}, eprintclass = {cs.CV}, pages = {13102--13112}, year = {2023}, keywords = {pnec, vo, vslam, deep learning}, } @inproceedings{lidarsynthesis2023, title = {LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels}, booktitle = {IEEE 26th International Conference on Intelligent Transportation Systems}, author = {J Schmidt and Q Khan and D Cremers}, year = {2023}, keywords = {neural networks, deep learning, lidar }, } @inproceedings{multiagent2023, title = {Multi Agent Navigation in Unconstrained Environments Using a Centralized Attention Based Graphical Neural Network Controller}, booktitle = {IEEE 26th International Conference on Intelligent Transportation Systems}, author = {Y Ma and Q Khan and D Cremers}, year = {2023}, keywords = {neural networks, deep learning, multi-agent control}, } @article{vehiclepursuit2023, author = {J Pan and C Zhou and M Gladkova and Q Khan and D Cremers}, title = {Robust Autonomous Vehicle Pursuit without Expert Steering Labels}, journal = {{IEEE} Robotics and Automation Letters ({RA-L})}, year = {2023}, volume = {8}, number = {10}, pages = {6595 - 6602}, keywords = {vehicle pursuit, deep learning}, } @inproceedings{xia2023casspr, author = {Y Xia and M Gladkova and R Wang and Q Li and U Stilla and JF. Henriques and D Cremers}, title = {CASSPR: Cross Attention Single Scan Place Recognition}, eprint = {2211.12542}, eprinttype = {arXiv}, eprintclass = {cs.CV}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2023}, keywords = {lidar, place recognition, deep learning}, } @inproceedings{xia2024text2loc, title = {Text2Loc: 3D Point Cloud Localization from Natural Language}, author = {Y Xia and L Shi and Z Ding and JF Henriques and D Cremers}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, keywords = {3d localization, deep learning, LiDAR point cloud}, year = {2024}, } @inproceedings{koke2024holonets, title = {HoloNets: Spectral Convolutions do extend to Directed Graphs}, author = {C Koke and D Cremers}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2024}, eprint = {2310.02232}, eprinttype = {arXiv}, journal = {arXiv preprint arXiv:2306.02099primaryClass=cs.LG}, } @inproceedings{koke2023holonets, title = {HoloNets: Spectral Convolutions do extend to Directed Graphs}, author = {C Koke and D Cremers}, booktitle = {NeurIPS 2023 Workshop: New Frontiers in Graph Learning}, award = {Oral Presentation}, year = {2023}, eprint = {2310.02232}, eprinttype = {arXiv}, journal = {arXiv preprint arXiv:2306.02099primaryClass=cs.LG}, } @inproceedings{koke2023resolvnet, title = {ResolvNet: A Graph Convolutional Network with multi-scale Consistency}, author = {C Koke and A Saroha and Y Shen and M Eisenberger and D Cremers}, booktitle = {NeurIPS 2023 Workshop: New Frontiers in Graph Learning}, award = {Oral Presentation}, year = {2023}, eprint = {2310.00431}, eprinttype = {arXiv}, journal = {arXiv preprint arXiv:2306.02099primaryClass=cs.LG}, } @inproceedings{schnaus2023learning, title = {Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks}, author = {D Schnaus and J Lee and D Cremers and R Triebel}, booktitle = {International Conference on Machine Learning}, pages = {30252--30284}, year = {2023}, organization = {PMLR}, } @inproceedings{gao2023sigma, author = {M Gao and P Roetzer and M Eisenberger and Z Lähner and M Moeller and D Cremers and F Bernard}, title = { {SIGMA}: Quantum Scale-Invariant Global Sparse Shape Matching}, booktitle = {International Conference on Computer Vision (ICCV)}, year = {2023}, keywords = {Shape Analysis, Geometry Processing, Global Optimisation, Mixed-Integer Programming, Laplace-Betrami}, } @inproceedings{Roetzer_et_al_cvpr22, title = {A scalable combinatorial solver for elastic geometrically consistent 3d shape matching}, author = {P Roetzer and P Swoboda and D Cremers and F Bernard}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages = {428--438}, year = {2022}, } @misc{deka2023erasing, title = {Erasing the Ephemeral: Joint Camera Refinement and Transient Object Removal for Street View Synthesis}, author = {MS Deka and L Sang and D Cremers}, year = {2023}, eprint = {2311.17634}, archiveprefix = {arXiv}, primaryclass = {cs.CV}, titleurl = {deka2023.png}, } @misc{komorowicz2023coloring, title = {Coloring the Past: Neural Historical Buildings Reconstruction from Archival Photography}, author = {D Komorowicz and L Sang and F Maiwald and D Cremers}, year = {2023}, eprint = {2311.17810}, archiveprefix = {arXiv}, primaryclass = {cs.CV}, titleurl = {komorowicz2023.png}, } @inproceedings{compreason2024, title = {Enhancing Multimodal Compositional Reasoning of Visual Language Models with Generative Negative Mining}, booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV}, author = {U Sahin and H Li and Q Khan and D Cremers and T Volker}, year = {2024}, keywords = {neural networks, deep learning, Large Language Models}, } @inproceedings{hayler2023s4c, title = {S4C: Self-Supervised Semantic Scene Completion with Neural Fields}, author = {A Hayler and F Wimbauer and D Muhle and C Rupprecht and D Cremers}, booktitle = {2024 International Conference on 3D Vision (3DV)}, year = {2024}, eprint = {2310.07522}, eprinttype = {arXiv}, eprintclass = {cs.CV}, } @article{ehm2023geometrically, title = {Geometrically Consistent Partial Shape Matching}, author = {V Ehm and P Roetzer and M Eisenberger and M Gao and F Bernard and D Cremers}, journal = {arXiv preprint arXiv:2309.05013}, titleurl = {2023_ehm_geo_cons.png}, year = {2023}, } @article{multivehicle2023, title = {Multi-vehicle trajectory prediction and control at intersections using state and intention information}, author = {D Zhu and Q Khan and D Cremers}, journal = {Neurocomputing}, pages = {127220}, year = {2024}, publisher = {Elsevier}, keywords = {deep learning}, } @inproceedings{solonets2024analytical, title = {An Analytical Solution to Gauss-Newton Loss for Direct Image Alignment}, author = {S Solonets and D Sinitsyn and L von Stumberg and N Araslanov and D Cremers}, booktitle = {{International Conference on Learning Representations (ICLR)}}, month = {May}, address = {Vienna, Austria}, year = {2024}, award = {Oral Presentation}, } @article{reich2023dvcc, title = {Deep Video Codec Control}, author = {C Reich and B Debnath and D Patel and T Prangemeier and D Cremers and S Chakradhar}, journal = {arXiv preprint arXiv:2308.16215}, year = {2023}, eprint = {2308.16215}, eprinttype = {arXiv}, eprintclass = {eess.IV}, titleurl = {reich2023dvcc.png}, } @inproceedings{weber2024finsler, title = {Finsler-Laplace-Beltrami Operators with Application to Shape Analysis}, author = {S Weber and T Dages and M Gao and D Cremers}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, keywords = {shape analysis, finsler manifold}, titleurl = {weber_finsler.png}, year = {2024}, } @inproceedings{weber2024hyperbolic, title = {Flattening the Parent Bias: Hierarchical Semantic Segmentation in the Poincaré Ball}, author = {S Weber and B Zöngür and N Araslanov and D Cremers}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, keywords = {segmentation, hyperbolic, calibration}, titleurl = {weber_hyperbolic.png}, year = {2024}, } @article{saroha2024gaussian, title = {Gaussian Splatting in Style}, author = {A Saroha and M Gladkova and C Curreli and T Yenamandra and D Cremers}, journal = {arXiv preprint arXiv:2403.08498}, year = {2024}, } @inproceedings{han2024kdbts, title = {Boosting Self-Supervision for Single-View Scene Completion via Knowledge Distillation}, author = {K Han and D Muhle and F Wimbauer and D Cremers}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, year = {2024}, }