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Technical University of Munich

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Books Book Chapters Journal Articles Preprints Conference and Workshop Papers PhD Thesis Technical Reports inproceedings 
Books
2011
[]Stereoscopic Scene Flow for 3D Motion Analysis (A. Wedel and D. Cremers), Springer, 2011.  [bibtex] [doi]
2007
[]Energy Minimization Methods for Computer Vision and Pattern Recognition (EMMCVPR) (Editors: S.-C. Zhu, A. Yuille, D. Cremers and Y. Wang), Springer, volume 4679, 2007.  [bibtex]
Books Book Chapters Journal Articles Preprints Conference and Workshop Papers PhD Thesis Technical Reports inproceedings 
Book Chapters
2019
[]A Review and Quantitative Evaluation of Direct Visual–Inertial Odometry (L. von Stumberg, V. Usenko and D. Cremers), Chapter in Multimodal Scene Understanding (Michael Ying Yang, Bodo Rosenhahn, Vittorio Murino, eds.), Elsevier, 2019.  [bibtex]
2016
[]Applying Random Forests to the Problem of Dense Non-rigid Shape Correspondence (Vestner, M., Rodolà, E., Windheuser, T., Bulò, Rota Bulo, S. and Cremers, D.), Chapter in Perspectives in Shape Analysis, Springer, 2016.  [bibtex]
2015
[]Holistic Image Reconstruction for Diffusion MRI (V. Golkov, J. M. Portegies, A. Golkov, R. Duits and D. Cremers), Chapter in Computational Diffusion MRI, Springer, 2015.  [bibtex] [pdf]Book Chapter, and Oral Presentation at MICCAI 2015 Workshop on Computational Diffusion MRI
2014
[]Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per q-Space Coordinate (V. Golkov, J.I. Sperl, M.I. Menzel, T. Sprenger, E.T. Tan, L. Marinelli, C.J. Hardy, A. Haase and D. Cremers), Chapter in Computational Diffusion MRI, Springer, 2014.  [bibtex] [pdf]Book Chapter, and Oral Presentation at MICCAI 2014 Workshop on Computational Diffusion MRI
2013
[]Moment Constraints in Convex Optimization for Segmentation and Tracking (M. Klodt, F. Steinbruecker and D. Cremers), Chapter in Advanced Topics in Computer Vision, Springer, 2013.  [bibtex] [pdf]
2012
[]A Sequential Monte Carlo Method for Multi-Target Tracking with the Intensity Filter (M. Schikora, W. Koch, R. L. Streit and D. Cremers), Chapter in Advances in Intelligent Signal Processing and Data Mining, Springer-Verlag Berlin Heidelberg, 2012.  [bibtex] [pdf]
2011
[]Image Segmentation with Shape Priors: Explicit Versus Implicit Representations (D. Cremers), Chapter in Handbook of Mathematical Methods in Imaging, Springer, 2011.  [bibtex] [pdf]
[]Convex Relaxation Techniques for Segmentation, Stereo and Multiview Reconstruction (D. Cremers, T. Pock, K. Kolev and A. Chambolle), Chapter in Markov Random Fields for Vision and Image Processing, MIT Press, 2011.  [bibtex] [pdf]
2010
[]An Introduction to Total Variation for Image Analysis (A. Chambolle, V. Caselles, D. Cremers, M. Novaga and T. Pock), Chapter in Theoretical Foundations and Numerical Methods for Sparse Recovery, De Gruyter, 2010.  [bibtex] [pdf]
2007
[]Efficient kernel density estimation of shape and intensity priors for level set segmentation (D. Cremers and M. Rousson), Chapter in Parametric and Geometric Deformable Models: An application in Biomaterials and Medical Imagery (J. S. Suri, A. Farag, eds.), Springer, 2007.  [bibtex] [pdf]
[]Contours, optic flow, and prior knowledge: cues for capturing 3D human motion in videos (T. Brox, B. Rosenhahn and D. Cremers), Chapter in Human Motion - Understanding, Modeling, Capture, and Animation, Springer, 2007.  [bibtex] [pdf]
2006
[]Integral Invariants and Shape Matching (S. Manay, D. Cremers, B. W. Hong, A. Yezzi and S. Soatto), Chapter in Statistical analysis of shapes (modeling and simulation in science, engineering and technology), Birkhauser, 2006.  [bibtex] [pdf]
[]Probabilistic kernel PCA and its application to statistical shape modeling and inference (D. Cremers and T. Kohlberger), Chapter in Kernel Methods in Bioengineering, Signal and Image Processing (G. Camps-Valls et al., ed.), Idea Group Inc., 2006.  [bibtex]
2005
[]Variational segmentation with shape priors (M. Bergtholdt, D. Cremers and C. Schnörr), Chapter in Handbook of Mathematical Models in Computer Vision (N. Paragios, Y. Chen, O. Faugeras, ed.), Springer, 2005.  [bibtex]
Books Book Chapters Journal Articles Preprints Conference and Workshop Papers PhD Thesis Technical Reports inproceedings 
Journal Articles
2019
[]Bregman Proximal Mappings and Bregman-Moreau Envelopes under Relative Prox-Regularity (Laude, E., Ochs, P. and Cremers, D.), In Journal of Optimization Theory and Applications, 2019. (to appear) [bibtex] [arXiv:1907.04306]
[]Photometric Depth Super-Resolution (Haefner, B., Peng, S., Verma, A., Quéau, Y. and Cremers, D.), In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019. ([supp] [project page] [ieeexplore]) [bibtex] [arXiv:1809.10097] [pdf]
[]A Non-invasive 3D Body Scanner and Software Tool towards Analysis of Scoliosis (S. Roy, A.T.D. Gruenwald, A. Alves-Pinto, R. Maier, D. Cremers, D. Pfeiffer and R. Lampe), In BioMed Research International (BMRI), 2019. ([pdf]) [bibtex]
[] Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization (F. Pasa, V. Golkov, F. Pfeiffer, D. Cremers and D. Pfeiffer), In Scientific Reports, volume 9, 2019.  [bibtex] [pdf] [doi]
2018
[]A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking (Tjaden, Henning, Schwanecke, Ulrich, Schömer, Elmar and Cremers, Daniel), In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.  [bibtex] [pdf]
[]Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform (L. Ma,, J. Stueckler, T. Wu and D. Cremers), In , 2018. ([arxiv]) [bibtex]
[]Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras (H. Matsuki, L. von Stumberg, V. Usenko, J. Stueckler and D. Cremers), In IEEE Robotics and Automation Letters & Int. Conference on Intelligent Robots and Systems (IROS), IEEE, 2018. ([arxiv]) [bibtex] [pdf]
[]Online Photometric Calibration of Auto Exposure Video for Realtime Visual Odometry and SLAM (P. Bergmann, R. Wang and D. Cremers), In IEEE Robotics and Automation Letters (RA-L), volume 3, 2018. (This paper was also selected by ICRA'18 for presentation at the conference.[arxiv][video][code][project]) [bibtex] [pdf]ICRA'18 Best Vision Paper Award - Finalist
[]Variational Reflectance Estimation from Multi-view Images (Mélou, J., Quéau, Y., Durou, J.-D., Castan, F. and Cremers, D.), In Journal of Mathematical Imaging and Vision, 2018. ([arxiv]) [bibtex] [doi] [pdf]
[]LED-based Photometric Stereo: Modeling, Calibration and Numerical Solution (Quéau, Y., Durix, B., Wu, T., Cremers, D., Lauze, F. and Durou, J.-D.), In Journal of Mathematical Imaging and Vision, volume 60, 2018. ([arxiv],[codes]) [bibtex] [doi] [pdf]
[]Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect (N. Yang, R. Wang, X. Gao and D. Cremers), In IEEE Robotics and Automation Letters (RA-L), volume 3, 2018. (This paper was also selected by IROS'18 for presentation at the conference.[arxiv]) [bibtex] [doi] [pdf]
[]Direct Sparse Odometry (J. Engel, V. Koltun and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.  [bibtex] [pdf]
2017
[]Tau Like Proteins Reduce Torque Generation in Microtubule Bundles (Krieg, Michael, Stühmer, Jan, Cueva, Juan G, Fetter, Richard, Spilker, Kerri, Cremers, Daniel, Shen, Kang, Dunn, Alex R and Goodman, Miriam B), In Biophysical Journal, Elsevier, volume 112, 2017.  [bibtex]
[]Genetic defects in ß-spectrin and tau sensitize C. elegans axons to movement-induced damage via torque-tension coupling (Krieg, Michael, Stühmer, Jan, Cueva, Juan G, Fetter, Richard, Spilker, Kerri, Cremers, Daniel, Shen, Kang, Dunn, Alexander R and Goodman, Miriam B), In eLife, eLife Sciences Publications Limited, volume 6, 2017.  [bibtex]
2016
[]Non-Rigid Puzzles (O. Litany, E. Rodola, A. M. Bronstein, M. M. Bronstein and D. Cremers), In Computer Graphics Forum, Wiley, volume 35, 2016.  [bibtex] [pdf]Received the Best Paper Award at SGP 2016
[]q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans (V. Golkov, A. Dosovitskiy, J. I. Sperl, M. I. Menzel, M. Czisch, P. Sämann, T. Brox and D. Cremers), In IEEE Transactions on Medical Imaging, volume 35, 2016. Special Issue on Deep Learning [bibtex] [pdf]Special Issue on Deep Learning
[]Anisotropic Diffusion Descriptors (D. Boscaini, J. Masci, E. Rodola, M. M. Bronstein and D. Cremers), In Computer Graphics Forum - Proc. EUROGRAPHICS, Wiley, volume 35, 2016.  [bibtex] [pdf]
[]Consistent Partial Matching of Shape Collections via Sparse Modeling (L. Cosmo, E. Rodola, A. Albarelli, F. Memoli and D. Cremers), In Computer Graphics Forum, Wiley, 2016.  [bibtex] [pdf]
[]Partial Functional Correspondence (E. Rodola, L. Cosmo, M. M. Bronstein, A. Torsello and D. Cremers), In Computer Graphics Forum, Wiley, 2016.  [bibtex] [pdf]
2015
[]The Primal-Dual Hybrid Gradient Method for Semiconvex Splittings (T. Möllenhoff, E. Strekalovskiy, M. Möller and D. Cremers), In SIAM Journal on Imaging Sciences, volume 8, 2015.  [bibtex] [pdf]
[]Realistic Photometric Stereo Using Partial Differential Irradiance Equation Ratios (R. Mecca, E. Rodola and D. Cremers), In Computers and Graphics, Elsevier, volume 51, 2015.  [bibtex] [doi] [pdf]
[]A Simple and Effective Relevance-based Point Sampling for 3D Shapes (E. Rodola, A. Albarelli, D. Cremers and A. Torsello), In Pattern Recognition Letters, Elsevier, volume 59, 2015.  [bibtex] [pdf] [code]
[]Field phenotyping of grapevine growth using dense stereo reconstruction (M. Klodt, K. Herzog, R. Töpfer and D. Cremers), In BMC Bioinformatics, volume 16, 2015.  [bibtex]
[]Car detection by fusion of HOG and causal MRF (S. Madhogaria, P. M. Baggenstoss, M. Schikora, W. Koch and D. Cremers), In IEEE T. on Aerospace and Electronic Systems, volume 51, 2015.  [bibtex]
[]The Role of Diffusion in Figure Hunt Games (J. Diebold, S. Tari and D. Cremers), In Journal of Mathematical Imaging and Vision, Springer, volume 52, 2015.  [bibtex] [doi] [pdf]
[] Midrange Geometric Interactions for Semantic Segmentation (J. Diebold, C. Nieuwenhuis and D. Cremers), In International Journal of Computer Vision, Springer US, 2015. Special Issue on Graphical Models for Scene Understanding [bibtex] [pdf] [doi] [pdf]
2014
[]Robust Region Detection via Consensus Segmentation of Deformable Shapes (E. Rodola, S. Rota Bulo and D. Cremers), In Computer Graphics Forum, Wiley, volume 33, 2014.  [bibtex] [pdf] [code]
[]Scale-Aware Navigation of a Low-Cost Quadrocopter with a Monocular Camera (J. Engel, J. Sturm and D. Cremers), In Robotics and Autonomous Systems (RAS), volume 62, 2014.  [bibtex] [pdf]
[]Convex Relaxation of Vectorial Problems with Coupled Regularization (E. Strekalovskiy, A. Chambolle and D. Cremers), In SIAM Journal on Imaging Sciences, volume 7, 2014.  [bibtex] [pdf]
[]A Super-resolution Framework for High-Accuracy Multiview Reconstruction (B. Goldluecke, M. Aubry, K. Kolev and D. Cremers), In International Journal of Computer Vision, volume 106, 2014.  [bibtex] [pdf]
2013
[]Introduction to the special issue on visual understanding and applications with RGB-D cameras (Liu, Z., Beetz, M., Cremers, D., Gall, J., Li, W., Pangercic, D., Sturm, J. and Tai, Y.-W.), In Journal of Visual Communication and Image Representation (JVCI), 2013.  [bibtex]
[]3D Mapping with an RGB-D Camera (F. Endres, J. Hess, J. Sturm, D. Cremers and W. Burgard), In IEEE Transactions on Robotics (T-RO), volume 30, 2013.  [bibtex] [pdf]
[]Tight Convex Relaxations for Vector-Valued Labeling (B. Goldluecke, E. Strekalovskiy and D. Cremers), In SIAM Journal on Imaging Sciences, volume 6, 2013.  [bibtex] [pdf]
[]A Survey and Comparison of Discrete and Continuous Multi-label Optimization Approaches for the Potts Model (C. Nieuwenhuis, E. Toeppe and D. Cremers), In International Journal of Computer Vision, volume 104, 2013. (Code available) [bibtex] [pdf]
[]Spatially Varying Color Distributions for Interactive Multi-Label Segmentation (C. Nieuwenhuis and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 35, 2013. (Code available) [bibtex] [pdf]
2012
[]Total Cyclic Variation and Generalizations (D. Cremers and E. Strekalovskiy), In Journal of Mathematical Imaging and Vision, volume 47, 2012.  [bibtex] [pdf]
[]An image classification approach to analyze the suppression of plant immunity by the human pathogen Salmonella Typhimurium (M. Schikora, B. Neupane, S. Madhogaria, W. Koch, D. Cremers, H. Hirt, K.-H. Kogel and A. Schikora), In BMC Bioinformatics, volume 13, 2012.  [bibtex] [pdf]
[]Fast Joint Estimation of Silhouettes and Dense 3D Geometry from Multiple Images (K. Kolev, T. Brox and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 34, 2012.  [bibtex] [pdf]
[]The Natural Total Variation Which Arises from Geometric Measure Theory (B. Goldluecke, E. Strekalovskiy and D. Cremers), In SIAM Journal on Imaging Sciences, volume 5, 2012.  [bibtex] [pdf]
[]Image segmentation with one shape prior - A template-based formulation (S. Chen, D. Cremers and R. J. Radke), In Image and Vision Computing, volume 30, 2012.  [bibtex] [pdf]
[]Optimal Solutions for Semantic Image Decomposition (D. Cremers), In Image and Vision Computing, volume 30, 2012.  [bibtex] [pdf]
[]A linear framework for region-based image segmentation and inpainting involving curvature penalization (T. Schoenemann, F. Kahl, S. Masnou and D. Cremers), In International Journal of Computer Vision, volume 99, 2012.  [bibtex] [pdf]
[]A Coding Cost Framework for Super-resolution Motion Layer Decomposition (T. Schoenemann and D. Cremers), In IEEE Transactions on Image Processing, volume 21, 2012.  [bibtex] [pdf]
[]A Convex Approach to Minimal Partitions (A. Chambolle, D. Cremers and T. Pock), In SIAM Journal on Imaging Sciences, volume 5, 2012.  [bibtex] [pdf]
2011
[]A Variational Approach to Vesicle Membrane Reconstruction from Fluorescence Imaging (K. Kolev, N. Kirchgessner, S. Houben, A. Csiszar, W. Rubner, C. Palm, B. Eiben, R. Merkel and D. Cremers), In Pattern Recognition, volume 44, 2011.  [bibtex] [pdf]
[]Motion Field Estimation from Alternate Exposure Images (A. Sellent, M. Eisemann, B. Goldluecke, D. Cremers and M. Magnor), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 33, 2011.  [bibtex] [pdf]
[]The Elastic Ratio: Introducing Curvature into Ratio-Based Globally Optimal Image Segmentation (T. Schoenemann, S. Masnou and D. Cremers), In IEEE Transactions on Image Processing, volume 20, 2011.  [bibtex] [pdf]
[]Stereoscopic Scene Flow Computation for 3D Motion Understanding (A. Wedel, T. Brox, T. Vaudrey, C. Rabe, U. Franke and D. Cremers), In International Journal of Computer Vision, volume 95, 2011.  [bibtex] [pdf]
[]Multiview Stereo and Silhouette Consistency via Convex Functionals over Convex Domains (D. Cremers and K. Kolev), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 33, 2011.  [bibtex] [pdf]
[]Large-Scale Integer Linear Programming for Orientation-Preserving 3D Shape Matching (T. Windheuser, U. Schlickewei, F. R. Schmidt and D. Cremers), In Computer Graphics Forum (Proceedings Symposium Geometry Processing), Eurographics, volume 30, 2011.  [bibtex] [pdf]
2010
[]A Combinatorial Solution for Model-based Image Segmentation and Real-time Tracking (T. Schoenemann and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 32, 2010.  [bibtex] [pdf]
[]Global Solutions of Variational Models with Convex Regularization (T. Pock, D. Cremers, H. Bischof and A. Chambolle), In SIAM Journal on Imaging Sciences, volume 3, 2010.  [bibtex] [pdf]
2009
[]B-Spline Modeling of Road Surfaces with an Application to Free Space Estimation (A. Wedel, C. Rabe, H. Badino, H. Loose, U. Franke and D. Cremers), In Transactions on Intelligent Transportation Systems, volume 10, 2009.  [bibtex] [pdf]
[]Continuous Global Optimization in Multiview 3D Reconstruction (K. Kolev, M. Klodt, T. Brox and D. Cremers), In International Journal of Computer Vision, volume 84, 2009.  [bibtex] [pdf]
[]Combined region- and motion-based 3D tracking of rigid and articulated objects (T. Brox, B. Rosenhahn, J. Gall and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 32, 2009.  [bibtex] [pdf]
[]On local region models and a statistical interpretation of the piecewise smooth Mumford-Shah functional (T. Brox and D. Cremers), In International Journal of Computer Vision, volume 84, 2009.  [bibtex] [pdf]
2008
[]3-D Reconstruction of Shaded Objects from Multiple Images Under Unknown Illumination (H. Jin, D. Cremers, D. Wang, A. Yezzi, E. Prados and S. Soatto), In International Journal of Computer Vision, volume 76, 2008.  [bibtex] [pdf]
[]Nonlinear Dynamical Shape Priors for Level Set Segmentation (D. Cremers), In Journal of Scientific Computing, volume 35, 2008.  [bibtex] [pdf]
[]Efficient Nonlocal Means for Denoising of Textural Patterns (T. Brox, O. Kleinschmidt and D. Cremers), In IEEE Transactions on Image Processing, volume 17, 2008.  [bibtex] [pdf]
2007
[]A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape (D. Cremers, M. Rousson and R. Deriche), In International Journal of Computer Vision, volume 72, 2007.  [bibtex] [pdf]
[]Computer Lernen Sehen (D. Cremers), In Industrial Vision, volume 2, 2007.  [bibtex] [pdf]
2006
[]Integral invariants for shape matching (S. Manay, D. Cremers, B.-W. Hong, A. Yezzi and S. Soatto), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 28, 2006.  [bibtex] [pdf]
[]A multiphase dynamic labeling model for variational recognition-driven image segmentation (D. Cremers, N. Sochen and C. Schnörr), In International Journal of Computer Vision, volume 66, 2006.  [bibtex] [pdf]
[]Kernel density estimation and intrinsic alignment for shape priors in level set segmentation (D. Cremers, S. J. Osher and S. Soatto), In International Journal of Computer Vision, volume 69, 2006.  [bibtex] [pdf]
[]Dynamical statistical shape priors for level set based tracking (D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 28, 2006.  [bibtex] [pdf]
2005
[]Motion Competition: A variational framework for piecewise parametric motion segmentation (D. Cremers and S. Soatto), In International Journal of Computer Vision, volume 62, 2005.  [bibtex] [pdf]
2003
[]Binary partitioning, perceptual grouping, and restoration with semidefinite programming (J. Keuchel, C. Schnörr, C. Schellewald and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 25, 2003.  [bibtex] [pdf]
[]Statistical shape knowledge in variational motion segmentation (D. Cremers and C. Schnörr), In Image and Vision Computing, volume 21, 2003.  [bibtex] [pdf]
[]Shape Statistics in Kernel Space for Variational Image Segmentation (D. Cremers, T. Kohlberger and C. Schnörr), In Pattern Recognition, volume 36, 2003.  [bibtex] [pdf]Awarded Best Paper of the Year 2003
2002
[]Diffusion Snakes: Introducing statistical shape knowledge into the Mumford–Shah functional (D. Cremers, F. Tischhäuser, J. Weickert and C. Schnörr), In International Journal of Computer Vision, volume 50, 2002.  [bibtex] [pdf]
[]Travelling waves of exitation in neural field models: Equivalence of rate descriptions and integrate-and-fire dynamics (D. Cremers and A. V. M. Herz), In Neural Computation, volume 14, 2002.  [bibtex] [pdf]
1999
[]Flow equations for the Héon-Heiles Hamiltonian (D. Cremers and A. Mielke), In Physica D, volume 126, 1999.  [bibtex] [pdf]
Books Book Chapters Journal Articles Preprints Conference and Workshop Papers PhD Thesis Technical Reports inproceedings 
Preprints
2019
[]Bregman Proximal Framework for Deep Linear Neural Networks (Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers and Peter Ochs), In arXiv preprint arXiv:1910.03638, 2019.  [bibtex] [arXiv:1910.03638]
[]Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods (L. Della Libera, V. Golkov, Y. Zhu, A. Mielke and D. Cremers), In arXiv preprint arXiv:1910.14594, 2019.  [bibtex] [arXiv:1910.14594] [pdf]
[]On well-posedness of uncalibrated photometric stereo under general lighting (Brahimi, M., Quéau, Y., Haefner, B. and Cremers, D.), In arXiv preprint arXiv:1911.07268, 2019.  [bibtex] [arXiv:1911.07268] [pdf]
[]Smooth Shells: Multi-Scale Shape Registration with Functional Maps (M. Eisenberger, Z. Lähner and D. Cremers), In preprint, 2019. (arXiv) [bibtex]
[]GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization (L. von Stumberg, P. Wenzel, Q. Khan and D. Cremers), In preprint, 2019. ([arxiv][video][project page][supplementary]) [bibtex]
[]Learning to Evolve (J. Schuchardt, V. Golkov and D. Cremers), In arXiv preprint arXiv:1905.03389, 2019.  [bibtex] [arXiv:1905.03389] [pdf]
[]DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation (R. Wang, N. Yang, J. Stueckler and D. Cremers), In preprint, 2019. ([arxiv][video] [project page]) [bibtex]
[]Homogeneous Linear Inequality Constraints for Neural Network Activations (T. Frerix, M. Nießner and D. Cremers), In preprint, 2019. (arXiv) [bibtex]
2018
[]Clustering with Deep Learning: Taxonomy and New Methods (E. Aljalbout, V. Golkov, Y. Siddiqui, M. Strobel and D. Cremers), In arXiv preprint arXiv:1801.07648, 2018.  [bibtex] [arXiv:1801.07648]
2017
[]Regularization for Deep Learning: A Taxonomy (J. Kukačka, V. Golkov and D. Cremers), In arXiv preprint arXiv:1710.10686, 2017.  [bibtex] [arXiv:1710.10686] [pdf]
[]3D Deep Learning for Biological Function Prediction from Physical Fields (V. Golkov, M. J. Skwark, A. Mirchev, G. Dikov, A. R. Geanes, J. Mendenhall, J. Meiler and D. Cremers), In arXiv preprint arXiv:1704.04039, 2017.  [bibtex] [arXiv:1704.04039] [pdf]
2016
[]Bayesian Inference of Bijective Non-Rigid Shape Correspondence (Vestner, M., Litman, R., Bronstein, A., Rodola, E. and Cremers, D.), In arXiv preprint arXiv:1607.03425, 2016. ([slides]) [bibtex] [pdf]
Books Book Chapters Journal Articles Preprints Conference and Workshop Papers PhD Thesis Technical Reports inproceedings 
Conference and Workshop Papers
2020
[]Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach (Sang, L., Haefner, B. and Cremers, D.), In IEEE Winter Conference on Applications of Computer Vision (WACV), 2020.  [bibtex]
2019
[]Informative GANs via Structured Regularization of Optimal Transport (P. Bréchet, T. Wu, T. Möllenhoff and D. Cremers), In NeurIPS Workshop on Optimal Transport and Machine Learning, 2019.  [bibtex] [arXiv:1912.02160]
[]Efficient Derivative Computation for Cumulative B-Splines on Lie Groups (C. Sommer, V. Usenko, D. Schubert, N. Demmel and D. Cremers), In arXiv:1911.08860, 2019.  [bibtex] [arXiv:1911.08860] [pdf]
[]Rolling-Shutter Modelling for Visual-Inertial Odometry (D. Schubert, N. Demmel, L. von Stumberg, V. Usenko and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2019. ([arxiv]) [bibtex] [pdf]
[]Sparse Surface Constraints for Combining Physics-based Elasticity Simulation and Correspondence-Free Object Reconstruction (S. Weiss, R. Maier, R. Westermann, D. Cremers and N. Thuerey), In arXiv preprint arXiv:1910.01812, 2019. ([pdf]) [bibtex] [arXiv:1910.01812]
[]Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light (E. Jung, N. Yang and D. Cremers), In Conference on Robot Learning (CoRL), 2019. ([arxiv],[supplementary]) [bibtex]Full Oral Presentation
[]Controlling Neural Networks via Energy Dissipation (M. Moeller, T. Möllenhoff and D. Cremers), In International Conference on Computer Vision (ICCV), 2019.  [bibtex] [arXiv:1904.03081]
[]Photometric Segmentation: Simultaneous Photometric Stereo and Masking (Haefner, B., Quéau, Y. and Cremers, D.), In International Conference on 3D Vision (3DV), 2019. ([poster]) [bibtex] [pdf]Spotlight Presentation
[]Towards Generalizing Sensorimotor Control Across Weather Conditions (Q. Khan, P. Wenzel, D. Cremers and L. Leal-Taixe), In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019. ([arxiv]) [bibtex] [pdf]
[]Variational Uncalibrated Photometric Stereo under General Lighting (Haefner, B., Ye, Z., Gao, M., Wu, T., Quéau, Y. and Cremers, D.), In International Conference on Computer Vision (ICCV), 2019. ([supp]) [bibtex] [arXiv:1904.03942] [pdf]
[]Divergence-Free Shape Correspondence by Deformation (M. Eisenberger, Z. Lähner and D. Cremers), In Computer Graphics Forum, 2019. ([arxiv]) [bibtex] [pdf]
[]Flat Metric Minimization with Applications in Generative Modeling (T. Möllenhoff and D. Cremers), In International Conference on Machine Learning (ICML), 2019. (arXiv:1905.04730, code, talk) [bibtex]Full Oral Presentation
[]Visual-Inertial Mapping with Non-Linear Factor Recovery (V. Usenko, N. Demmel, D. Schubert, J. Stueckler and D. Cremers), In arXiv:1904.06504, 2019.  [bibtex] [arXiv:1904.06504] [pdf]
[]Optimization of Inf-Convolution Regularized Nonconvex Composite Problems (E. Laude, T. Wu and D. Cremers), In International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.  [bibtex] [pdf]
[]Lifting Vectorial Variational Problems: A Natural Formulation based on Geometric Measure Theory and Discrete Exterior Calculus (T. Möllenhoff and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. (arXiv:1905.00851, code, talk) [bibtex] [pdf]Oral Presentation
[]Negative-Unlabeled Learning for Diffusion MRI (P. Swazinna, V. Golkov, I. Lipp, E. Sgarlata, V. Tomassini, D. K. Jones and D. Cremers), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2019.  [bibtex] [pdf]
[]q-Space Novelty Detection with Variational Autoencoders (A. Vasilev, V. Golkov, M. Meissner, I. Lipp, E. Sgarlata, V. Tomassini, D. K. Jones and D. Cremers), In MICCAI 2019 International Workshop on Computational Diffusion MRI, 2019.  [bibtex] [arXiv:1806.02997] [pdf]Oral Presentation
2018
[]Fight ill-posedness with ill-posedness: Single-shot variational depth super-resolution from shading (Haefner, B., Quéau, Y., Möllenhoff, T. and Cremers, D.), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. ([supp] [poster] [code] [cvf] [video]) [bibtex] [pdf] [video]Spotlight Presentation
[]Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs (P. Wenzel, Q. Khan, D. Cremers and L. Leal-Taixé), In Conference on Robot Learning (CoRL), 2018. ([arXiv][videos][poster]) [bibtex] [pdf]
[]Robust Fitting of Subdivision Surfaces for Smooth Shape Analysis (V. Estellers, F. Schmidt and D. Cremers), In Proc. of the Int. Conference on 3D Vision (3DV), 2018. ([Code]) [bibtex] [pdf]Received the Best Paper Award at 3DV 2018
[]Incremental Semi-Supervised Learning from Streams for Object Classification (I. Chiotellis, F. Zimmermann, D. Cremers and R. Triebel), In International Conference on Intelligent Robots and Systems (IROS), 2018. ([code]) [bibtex] [pdf]
[]The Double Sphere Camera Model (V. Usenko, N. Demmel and D. Cremers), In Proc. of the Int. Conference on 3D Vision (3DV), 2018. ([arxiv]) [bibtex] [arXiv:1807.08957] [pdf]
[]Direct Sparse Odometry With Rolling Shutter (D. Schubert, N. Demmel, V. Usenko, J. Stueckler and D. Cremers), In European Conference on Computer Vision (ECCV), 2018. ([supplementary][arxiv]) [bibtex] [pdf]Oral Presentation
[]Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry (N. Yang, R. Wang, J. Stueckler and D. Cremers), In European Conference on Computer Vision (ECCV), 2018. ([arxiv],[supplementary],[video],[talk],[project]) [bibtex]Oral Presentation
[]DeepWrinkles: Accurate and Realistic Clothing Modeling (Z. Lähner, D. Cremers and T. Tung), In European Conference on Computer Vision (ECCV), 2018. ([Homepage], [Oral Presentation]) [bibtex] [pdf]Oral Presentation
[]LDSO: Direct Sparse Odometry with Loop Closure (X. Gao, R. Wang, N. Demmel and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2018. ([arxiv][video][code][project]) [bibtex]
[]The TUM VI Benchmark for Evaluating Visual-Inertial Odometry (D. Schubert, T. Goll, N. Demmel, V. Usenko, J. Stueckler and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2018. ([arxiv]) [bibtex]
[]Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization (L. von Stumberg, V. Usenko and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2018. ([supplementary][video][arxiv]) [bibtex] [pdf]
[]Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs (E. Laude, J.-H. Lange, J. Schüpfer, C. Domokos, L. Leal-Taixé, F. R. Schmidt, B. Andres and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.  [bibtex] [pdf]
[]Proximal Backpropagation (T. Frerix, T. Möllenhoff, M. Moeller and D. Cremers), In International Conference on Learning Representations (ICLR), 2018. (arXiv:1706.04638, code) [bibtex]
[]What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation? (Nikolaus Mayer, Eddy Ilg, Philipp Fischer, Caner Hazirbas, Daniel Cremers, Alexey Dosovitskiy and Thomas Brox), In International Journal of Computer Vision, 2018. (arxiv) [bibtex] [arXiv:1801.06397]
[]Associative Deep Clustering - Training a Classification Network with no Labels (P. Haeusser, J. Plapp, V. Golkov, E. Aljalbout and D. Cremers), In Proc. of the German Conference on Pattern Recognition (GCPR), 2018.  [bibtex] [pdf]
[]Precursor microRNA Identification Using Deep Convolutional Neural Networks (B. T. Do, V. Golkov, G. E. Gürel and D. Cremers), In bioRxiv preprint, 2018. (bioRxiv:414656) [bibtex] [pdf]
[]q-Space Deep Learning for Alzheimer's Disease Diagnosis: Global Prediction and Weakly-Supervised Localization (V. Golkov, P. Swazinna, M. M. Schmitt, Q. A. Khan, C. M. W. Tax, M. Serahlazau, F. Pasa, F. Pfeiffer, G. J. Biessels, A. Leemans and D. Cremers), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2018.  [bibtex] [pdf]
[]q-Space Novelty Detection in Short Diffusion MRI Scans of Multiple Sclerosis (V. Golkov, A. Vasilev, F. Pasa, I. Lipp, W. Boubaker, E. Sgarlata, F. Pfeiffer, V. Tomassini, D. K. Jones and D. Cremers), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2018.  [bibtex] [pdf]
[]Combinatorial Preconditioners for Proximal Algorithms on Graphs (T. Möllenhoff, Z. Ye, T. Wu and D. Cremers), In International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.  [bibtex] [pdf]
[]A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization (E. Laude, T. Wu and D. Cremers), In International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.  [bibtex] [pdf]
[]Deep Depth From Focus (Caner Hazirbas, Sebastian Georg Soyer, Maximilian Christian Staab, Laura Leal-Taixé and Daniel Cremers), In Asian Conference on Computer Vision (ACCV), 2018. ([arxiv], Deep Depth From Focus,[dataset]) [bibtex]
2017
[]Depth Super-Resolution Meets Uncalibrated Photometric Stereo (Peng, S., Haefner, B., Quéau, Y. and Cremers, D.), In International Conference on Computer Vision Workshops (ICCVW), 2017. ([code] [cvf]) [bibtex] [arXiv:1708.00411] [pdf]Oral Presentation at ICCV Workshop on Color and Photometry in Computer Vision
[]A Combinatorial Solution to Non-Rigid 3D Shape-to-Image Matching (F. Bernard, F. R. Schmidt, J. Thunberg and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.  [bibtex] [pdf]
[]A Variational Approach to Shape-from-shading Under Natural Illumination (Quéau, Y., Mélou, J., Castan, F., Cremers, D. and Durou, J.-D.), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2017. ([codes]) [bibtex] [doi] [pdf]
[]Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity Problems (T. Möllenhoff and D. Cremers), In International Conference on Computer Vision (ICCV), 2017. ([supp]) [bibtex] [pdf]
[]Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras (R. Wang, M. Schwörer and D. Cremers), In International Conference on Computer Vision (ICCV), 2017. ([supplementary][video][arxiv][project]) [bibtex] [pdf]
[]Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting (R. Maier, K. Kim, D. Cremers, J. Kautz and M. Niessner), In International Conference on Computer Vision (ICCV), 2017. ([slides] [poster] [dataset] [code]) [bibtex] [pdf]
[]Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction (R. Maier, R. Schaller and D. Cremers), In British Machine Vision Conference (BMVC), 2017. ([poster] [supplementary]) [bibtex] [pdf]
[]Efficient Deformable Shape Correspondence via Kernel Matching (M. Vestner, Z. Lähner, A. Boyarski, O. Litany, R. Slossberg, T. Remez, E. Rodolà, A. M. Bronstein, M. M. Bronstein, R. Kimmel and D. Cremers), In International Conference on 3D Vision (3DV), 2017. ([arxiv],[Code]) [bibtex] [pdf]Oral Presentation
[]Stéréophotométrie microscopique sans démosaïquage (Quéau, Y., Pizenberg, M., Cremers, D. and Durou, J.-D.), In GRETSI, 2017.  [bibtex] [pdf]
[]Associative Domain Adaptation (P. Haeusser, T. Frerix, A. Mordvintsev and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2017. ([code] [PDF from CVF]) [bibtex] [pdf]
[]Dense Multi-view 3D-reconstruction Without Dense Correspondences (Quéau, Y., Mélou, J., Durou, J.-D. and Cremers, D.), In ArXiv preprint 1704.00337, 2017. ([arxiv]) [bibtex] [pdf]
[]One-Shot Video Object Segmentation (S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers and L. Van Gool), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.  [bibtex] [pdf]
[]Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems (Tim Meinhardt, Michael Moeller, Caner Hazirbas and Daniel Cremers), In IEEE International Conference on Computer Vision (ICCV), 2017. ([arxiv], [code]) [bibtex]
[]A Non-Convex Variational Approach to Photometric Stereo under Inaccurate Lighting (Quéau, Y., Wu, T., Lauze, F., Durou, J.-D. and Cremers, D.), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ([codes]) [bibtex] [pdf]
[]Beyond Multi-view Stereo: Shading-Reflectance Decomposition (Mélou, J., Quéau, Y., Durou, J.-D., Castan, F. and Cremers, D), In International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), 2017.  [bibtex] [pdf]
[]Semi-Calibrated Near-Light Photometric Stereo (Quéau, Y., Wu, T. and Cremers, D.), In International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), 2017. ([codes]) [bibtex] [pdf]
[]Real-Time Trajectory Replanning for MAVs using Uniform B-splines and a 3D Circular Buffer (V. Usenko, L. von Stumberg, A. Pangercic and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2017. ([arxiv]) [bibtex] [pdf]Best Paper Award - Finalist (link)
[]KillingFusion: Non-rigid 3D Reconstruction without Correspondences (Miroslava Slavcheva, Maximilian Baust, Daniel Cremers and Slobodan Ilic), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.  [bibtex] [pdf]
[]Learning by Association - A versatile semi-supervised training method for neural networks (P. Haeusser, A. Mordvintsev and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ([code] [PDF from CVF]) [bibtex] [pdf]
[]Microgeometry capture and RGB albedo estimation by photometric stereo without demosaicing (Quéau, Y., Pizenberg, M., Durou, J.-D. and Cremers, D.), In International Conference on Quality Control by Artificial Vision (QCAV), 2017. ([codes]) [bibtex] [pdf]
[]Establishment of an interdisciplinary workflow of machine learning-based Radiomics in sarcoma patients (J.C. Peeken, C. Knie, V. Golkov, K. Kessel, F. Pasa, Q. Khan, M. Seroglazov, J. Kukačka, T. Goldberg, L. Richter, J. Reeb, B. Rost, F. Pfeiffer, D. Cremers, F. Nüsslin and S.E. Combs), In 23. Jahrestagung der Deutschen Gesellschaft für Radioonkologie (DEGRO), 2017.  [bibtex]
[]Image-based localization using LSTMs for structured feature correlation (Florian Walch, Caner Hazirbas, Laura Leal-Taixé, Torsten Sattler, Sebastian Hilsenbeck and Daniel Cremers), In IEEE International Conference on Computer Vision (ICCV), 2017. ([arxiv]) [bibtex]
[]From Monocular SLAM to Autonomous Drone Exploration (L. von Stumberg, V. Usenko, J. Engel, J. Stueckler and D. Cremers), In European Conference on Mobile Robots (ECMR), 2017.  [bibtex] [pdf]
[]De-noising, Stabilizing and Completing 3D Reconstructions On-the-go using Plane Priors (M. Dzitsiuk, J. Sturm, R. Maier, L. Ma and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2017. ([video]) [bibtex] [pdf]
[]Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel Density Estimation in the Product Space (Vestner, M., Litman, R., Rodola, E., Bronstein, A. and Cremers, D.), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ([Code], also check the related github repository) [bibtex] [pdf]
[]Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras (L. Ma, J. Stueckler, C. Kerl and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2017.  [bibtex] [pdf]
[]An Efficient Background Term for 3D Reconstruction and Tracking with Smooth Subdivision Surface Models (M. Jaimez, T. J. Cashman, A. Fitzgibbon, J. Gonzalez-Jimenez and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ([video]) [bibtex] [pdf]
[]Fast Odometry and Scene Flow from RGB-D Cameras based on Geometric Clustering (M. Jaimez, C. Kerl, J. Gonzalez-Jimenez and D. Cremers), In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), 2017. ([video]) [bibtex] [pdf]
2016
[]Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks (S. Sharifzadeh, I. Chiotellis, R. Triebel and D. Cremers), In , NIPS Workshops, 2016. ([arxiv]) [bibtex] [pdf]
[]A Convex Solution to Spatially-Regularized Correspondence Problems (T. Windheuser and D. Cremers), In European Conference on Computer Vision (ECCV), 2016.  [bibtex] [pdf]
[]FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture (C. Hazirbas, L. Ma, C. Domokos and D. Cremers), In Asian Conference on Computer Vision, 2016. ([code]) [bibtex] [pdf]
[]Non-Rigid 3D Shape Retrieval via Large Margin Nearest Neighbor Embedding (I. Chiotellis, R. Triebel, T. Windheuser and D. Cremers), In European Conference on Computer Vision (ECCV), 2016. ([code]) [bibtex] [pdf]
[]Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies (E. Laude, T. Möllenhoff, M. Moeller, J. Lellmann and D. Cremers), In European Conference on Computer Vision (ECCV), 2016. ([supp] [code]) [bibtex] [pdf]
[]Direct Sparse Odometry (J. Engel, V. Koltun and D. Cremers), In arXiv:1607.02565, 2016.  [bibtex] [pdf]
[]A Photometrically Calibrated Benchmark For Monocular Visual Odometry (J. Engel, V. Usenko and D. Cremers), In arXiv:1607.02555, 2016.  [bibtex] [pdf]
[]CPA-SLAM: Consistent Plane-Model Alignment for Direct RGB-D SLAM (L. Ma, C. Kerl, J. Stueckler and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2016.  [bibtex] [pdf]
[]Sublabel-Accurate Relaxation of Nonconvex Energies (T. Möllenhoff, E. Laude, M. Moeller, J. Lellmann and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ([supp] [code]) [bibtex] [pdf]Oral Presentation, Received the Best Paper Honorable Mention Award at CVPR 2016
[]SHREC’16: Partial Matching of Deformable Shapes (L. Cosmo, E. Rodola, M. M. Bronstein, A. Torsello, D. Cremers and Y. Sahillioglu), In Proc. of Eurographics Workshop on 3D Object Retrieval (3DOR), 2016. (to appear) [bibtex] [pdf]
[] SHREC’16: Matching of Deformable Shapes with Topological Noise (Z. Lähner, E. Rodola, M. M. Bronstein, D. Cremers, O. Burghard, L. Cosmo, A. Dieckmann, R. Klein and Y. Sahillioglu), In Proc. of Eurographics Workshop on 3D Object Retrieval (3DOR), 2016. ([Dataset]) [bibtex] [pdf] [pdf]
[]Stream-based Active Learning for Efficient and Adaptive Classification of 3D Objects (A. Narr, R. Triebel and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2016.  [bibtex] [pdf]
[]Direct Visual-Inertial Odometry with Stereo Cameras (V. Usenko, J. Engel, J. Stueckler and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2016.  [bibtex] [pdf] [video]
[] Efficient Globally Optimal 2D-to-3D Deformable Shape Matching (Z. Lähner, E. Rodola, F. R. Schmidt, M. M. Bronstein and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ([Code], [Homepage]) [bibtex] [pdf] [pdf]
[]Protein Contact Prediction from Amino Acid Co-Evolution Using Convolutional Networks for Graph-Valued Images (V. Golkov, M. J. Skwark, A. Golkov, A. Dosovitskiy, T. Brox, J. Meiler and D. Cremers), In Annual Conference on Neural Information Processing Systems (NIPS), 2016. ([video]) [bibtex] [pdf]Oral Presentation (acceptance rate: under 2%)
[]Model-Free Novelty-Based Diffusion MRI (V. Golkov, T. Sprenger, J. I. Sperl, M. I. Menzel, M. Czisch, P. Sämann and D. Cremers), In IEEE International Symposium on Biomedical Imaging (ISBI), 2016.  [bibtex] [pdf]
2015
[]Model-Based Tracking at 300Hz using Raw Time-of-Flight Observations (J. Stühmer, S. Nowozin, A. Fitzgibbon, R. Szeliski, T. Perry, S. Acharya, D. Cremers and J. Shotton), In IEEE International Conference on Computer Vision (ICCV), 2015. ([video]) [bibtex] [pdf]
[]CAPTCHA Recognition with Active Deep Learning (F. Stark, C. Hazirbas, R. Triebel and D. Cremers), In GCPR Workshop on New Challenges in Neural Computation, 2015. ([code]) [bibtex] [pdf]
[]Entropy Minimization for Convex Relaxation Approaches (M. Souiai, M. R. Oswald, Y. Kee, J. Kim, M. Pollefeys and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2015. (accepted) [bibtex] [pdf]
[]Dense Continuous-Time Tracking and Mapping with Rolling Shutter RGB-D Cameras (C. Kerl, J. Stueckler and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2015. ([video][supplementary][datasets]) [bibtex] [pdf]
[]Point-wise Map Recovery and Refinement from Functional Correspondence (E. Rodola, M. Moeller and D. Cremers), In Proceedings Vision, Modeling and Visualization (VMV), 2015.  [bibtex] [pdf]Received the Best Paper Award
[]Motion Cooperation: Smooth Piece-Wise Rigid Scene Flow from RGB-D Images (M. Jaimez, M. Souiai, J. Stueckler, J. Gonzalez-Jimenez and D. Cremers), In Proc. of the Int. Conference on 3D Vision (3DV), 2015. ([video]) [bibtex] [pdf]
[]Reconstructing Street-Scenes in Real-Time From a Driving Car (V. Usenko, J. Engel, J. Stueckler and D. Cremers), In Proc. of the Int. Conference on 3D Vision (3DV), 2015.  [bibtex] [pdf]
[]Super-Resolution Keyframe Fusion for 3D Modeling with High-Quality Textures (R. Maier, J. Stueckler and D. Cremers), In International Conference on 3D Vision (3DV), 2015. ([slides] [poster]) [bibtex] [pdf]
[]Semi-supervised Online Learning for Efficient Classification of Objects in 3D Data Streams (Y. Tao, R. Triebel and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2015.  [bibtex] [pdf]
[]Large-Scale Direct SLAM for Omnidirectional Cameras (D. Caruso, J. Engel and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2015.  [bibtex] [pdf] [video]
[]Large-Scale Direct SLAM with Stereo Cameras (J. Engel, J. Stueckler and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2015.  [bibtex] [pdf] [video]
[]SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports (R. Triebel, K. Arras, R. Alami, L. Beyer, S. Breuers, R. Chatila, M. Chetouani, D. Cremers, V. Evers, M. Fiore, H. Hung, O. A. Islas Ramírez, M. Joosse, H. Khambhaita, T. Kucner, B. Leibe, A. J. Lilienthal, T. Linder, M. Lohse, M. Magnusson, B. Okal, L. Palmieri, U. Rafi, M. van Rooij and L. Zhang), In Proc. Field and Service Robotics (FSR), 2015.  [bibtex] [pdf]
[]FlowNet: Learning Optical Flow with Convolutional Networks (A. Dosovitskiy, P. Fischer, E. Ilg, P. Haeusser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers and T. Brox), In IEEE International Conference on Computer Vision (ICCV), 2015. ([video],[code]) [bibtex] [doi] [pdf]
[]q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans (V. Golkov, A. Dosovitskiy, P. Sämann, J. I. Sperl, T. Sprenger, M. Czisch, M. I. Menzel, P. A. Gómez, A. Haase, T. Brox and D. Cremers), In Medical Image Computing and Computer Assisted Intervention (MICCAI), 2015.  [bibtex] [pdf]
[]Active Online Confidence Boosting for Efficient Object Classification (D. Mund, R. Triebel and D. Cremers), In Proc. IEEE International Conference on Robotics and Automation (ICRA), 2015.  [bibtex] [pdf]
[]Adopting an Unconstrained Ray Model in Light-field Cameras for 3D Shape Reconstruction (F. Bergamasco, A. Albarelli, L. Cosmo, A. Torsello, E. Rodola and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.  [bibtex] [pdf]
[]Analysis of Surface Parametrizations for Modern Photometric Stereo Modeling (R. Mecca, E. Rodola and D. Cremers), In International Conference on Quality Control by Artificial Vision (QCAV), 2015.  [bibtex] [pdf]
[]A Fast Projection Method for Connectivity Constraints in Image Segmentation (J. Stühmer and D. Cremers), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR) (X.-C. Tai, E. Bae, T. F. Chan, M. Lysaker, eds.), 2015.  [bibtex] [pdf]
[]A Primal-Dual Framework for Real-Time Dense RGB-D Scene Flow (M. Jaimez, M. Souiai, J. Gonzalez-Jimenez and D. Cremers), In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), 2015. ([video]) [bibtex] [pdf]
[]Low Rank Priors for Color Image Regularization (T. Möllenhoff, E. Strekalovskiy, M. Möller and D. Cremers), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2015.  [bibtex] [pdf]
[]Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation (C. Hazirbas, J. Diebold and D. Cremers), In Scale Space and Variational Methods in Computer Vision (SSVM), 2015. ([code]) [bibtex] [doi] [pdf]Oral Presentation
[]Interactive Multi-label Segmentation of RGB-D Images (J. Diebold, N. Demmel, C. Hazirbas, M. Möller and D. Cremers), In Scale Space and Variational Methods in Computer Vision (SSVM), 2015. ([code]) [bibtex] [doi] [pdf]
[]Learning Nonlinear Spectral Filters for Color Image Reconstruction (M. Moeller, J. Diebold, G. Gilboa and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2015.  [bibtex] [pdf]
2014
[]Dense Elastic 3D Shape Matching (F. R. Schmidt, T. Windheuser, U. Schlickewei and D. Cremers), In Global Optimization Methods, Springer, volume 8293, 2014.  [bibtex] [pdf]
[]Towards Illumination-invariant 3D Reconstruction using ToF RGB-D Cameras (C. Kerl, M. Souiai, J. Sturm and D. Cremers), In International Conference on 3D Vision (3DV), 2014. ([supplementary]) [bibtex] [pdf]
[]INS-Camera Calibration without Ground Control Points (D. Bender, M. Schikora, J. Sturm and D. Cremers), In 9th IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2014.  [bibtex] [pdf]
[]Learning Similarities for Rigid and Non-Rigid Object Detection (A. Kanezaki, E. Rodola, D. Cremers and T. Harada), In International Conference on 3D Vision (3DV), 2014.  [bibtex] [pdf]
[]Environment-adaptive Learning: How Clustering Helps to Obtain Good Training Data (S. Debnath, S. S. Baishya, R. Triebel, V. Dutt and D. Cremers), In KI 2014: Advances in Artificial Intelligence (Carsten Lutz, Michael Thielscher, eds.), Springer, 2014.  [bibtex] [pdf]
[]Active Online Learning for Interactive Segmentation Using Sparse Gaussian Processes (R. Triebel, J. Stühmer, M. Souiai and D. Cremers), In German Conference on Pattern Recognition, 2014.  [bibtex] [pdf]
[]Visual-Inertial Navigation for a Camera-Equipped 25g Nano-Quadrotor (O. Dunkley, J. Engel, J. Sturm and D. Cremers), In IROS2014 Aerial Open Source Robotics Workshop, 2014.  [bibtex] [pdf] [video]
[]Anisotropic Laplace-Beltrami Operators for Shape Analysis (M. Andreux, E. Rodola, M. Aubry and D. Cremers), In Sixth Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment (NORDIA), 2014.  [bibtex] [pdf]
[]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] (A. Kanezaki, E. Rodola, D. Cremers and T. Harada), In 信学技報 - Pattern Recognition and Media Understanding (PRMU), volume 114, 2014.  [bibtex] [pdf]
[]Real-Time Minimization of the Piecewise Smooth Mumford-Shah Functional (E. Strekalovskiy and D. Cremers), In European Conference on Computer Vision (ECCV), 2014. (Code available) [bibtex] [pdf] [video]
[]Generalized Connectivity Constraints for Spatio-temporal 3D Reconstruction (M. R. Oswald, J. Stühmer and D. Cremers), In European Conference on Computer Vision (ECCV), 2014.  [bibtex] [pdf] [video]
[]Co-Sparse Textural Similarity for Interactive Segmentation (C. Nieuwenhuis, S. Hawe, M. Kleinsteuber and D. Cremers), In European Conference on Computer Vision (ECCV), 2014.  [bibtex] [pdf]
[]Surface Normal Integration for Convex Space-time Multi-view Reconstruction (M. R. Oswald and D. Cremers), In British Machine Vision Conference (BMVC), 2014.  [bibtex] [pdf] [video]
[]Spatial and Temporal Interpolation of Multi-View Image Sequences (T. Gurdan, M. R. Oswald, D. Gurdan and D. Cremers), In German Conference on Pattern Recognition (GCPR), volume 36, 2014.  [bibtex] [pdf] [video]
[]Submap-based Bundle Adjustment for 3D Reconstruction from RGB-D Data (R. Maier, J. Sturm and D. Cremers), In German Conference on Pattern Recognition (GCPR), 2014. ([slides]) [bibtex] [pdf]Oral Presentation
[]Flow and Color Inpainting for Video Completion (M. Strobel, J. Diebold and D. Cremers), In German Conference on Pattern Recognition (GCPR), 2014.  [bibtex] [doi] [pdf]Oral Presentation
[]Optimal Intrinsic Descriptors for Non-Rigid Shape Analysis (T. Windheuser, M. Vestner, E. Rodola, R. Triebel and D. Cremers), In British Machine Vision Conference (BMVC), 2014.  [bibtex] [pdf]
[]Semi-Dense Visual Odometry for AR on a Smartphone (T. Schöps, J. Engel and D. Cremers), In International Symposium on Mixed and Augmented Reality, 2014.  [bibtex] [pdf] [video]Best Short Paper Award
[]LSD-SLAM: Large-Scale Direct Monocular SLAM (J. Engel, T. Schöps and D. Cremers), In European Conference on Computer Vision (ECCV), 2014.  [bibtex] [pdf] [video]Oral Presentation
[]Collision Avoidance for Quadrotors with a Monocular Camera (H. Alvarez, L.M. Paz, J. Sturm and D. Cremers), In Proc. of The 12th International Symposium on Experimental Robotics (ISER), 2014.  [bibtex] [pdf]
[]Sequential Convex Relaxation for Mutual-Information-Based Unsupervised Figure-Ground Segmentation (Y. Kee, M. Souiai, D. Cremers and J. Kim), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.  [bibtex] [pdf]
[]Dense Non-Rigid Shape Correspondence Using Random Forests (E. Rodola, S. Rota Bulo, T. Windheuser, M. Vestner and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.  [bibtex] [pdf] [code]
[]Volumetric 3D Mapping in Real-Time on a CPU (F. Steinbruecker, J. Sturm and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2014.  [bibtex] [pdf]
[]Improved Diffusion Kurtosis Imaging and Direct Propagator Estimation Using 6-D Compressed Sensing (V. Golkov, M.I. Menzel, T. Sprenger, M. Souiai, A. Haase, D. Cremers and J.I. Sperl), In Organization for Human Brain Mapping (OHBM) Annual Meeting, 2014.  [bibtex]
[]Semi-Joint Reconstruction for Diffusion MRI Denoising Imposing Similarity of Edges in Similar Diffusion-Weighted Images (V. Golkov, M.I. Menzel, T. Sprenger, A. Haase, D. Cremers and J.I. Sperl), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2014.  [bibtex]
[]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 (V. Golkov, M.I. Menzel, T. Sprenger, M. Souiai, A. Haase, D. Cremers and J.I. Sperl), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2014.  [bibtex]
2013
[]Relaxations for Minimizing Metric Distortion and Elastic Energies for 3D Shape Matching (D. Cremers, E. Rodola and T. Windheuser), In Actes des recontres du CIRM: Courbure discrete: théorie et applications, volume 3, 2013.  [bibtex] [pdf]
[]Interactive Person Following and Gesture Recognition with a Flying Robot (T. Naseer, J. Sturm and D. Cremers), In Proc. of the Assistance and Service Robotics Workshop (ASROB) at the IEEE. Int. Conf. on Intelligent Robots and Systems (IROS), 2013.  [bibtex] [pdf]
[]Large-Scale Multi-Resolution Surface Reconstruction from RGB-D Sequences (F. Steinbruecker, C. Kerl, J. Sturm and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2013.  [bibtex] [pdf]
[]A Convex Relaxation Approach to Space Time Multi-view 3D Reconstruction (M. R. Oswald and D. Cremers), In ICCV Workshop on Dynamic Shape Capture and Analysis (4DMOD), 2013.  [bibtex] [pdf]
[]Fast and Accurate Large-scale Stereo Reconstruction using Variational Methods (G. Kuschk and D. Cremers), In ICCV Workshop on Big Data in 3D Computer Vision, 2013.  [bibtex] [pdf]
[]Tree Shape Priors with Connectivity Constraints using Convex Relaxation on General Graphs (J. Stühmer, P. Schröder and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2013.  [bibtex] [pdf]Oral Presentation
[]Proportion Priors for Image Sequence Segmentation (C. Nieuwenhuis, E. Strekalovskiy and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2013. (oral presentation) [bibtex] [pdf] [video]
[]Total Variation Regularization for Functions with Values in a Manifold (J. Lellmann, E. Strekalovskiy, S. Koetter and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2013.  [bibtex] [pdf]
[]Elastic Net Constraints for Shape Matching (E. Rodola, A. Torsello, T. Harada, Y. Kuniyoshi and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2013.  [bibtex] [pdf] [code]
[]Semi-Dense Visual Odometry for a Monocular Camera (J. Engel, J. Sturm and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2013.  [bibtex] [pdf] [video]
[]Efficient Shape Matching using Vector Extrapolation (E. Rodola, T. Harada, Y. Kuniyoshi and D. Cremers), In British Machine Vision Conference (BMVC), 2013.  [bibtex] [pdf]
[]CopyMe3D: Scanning and Printing Persons in 3D (J. Sturm, E. Bylow, F. Kahl and D. Cremers), In German Conference on Pattern Recognition (GCPR), 2013.  [bibtex] [pdf]
[]Graph-based bundle adjustment for INS-camera calibration (D. Bender, M. Schikora, J. Sturm and D. Cremers), In Unmanned Aerial Vehicle in Geomatics (UAV-g), 2013.  [bibtex] [pdf]Best research paper award
[]Dense Tracking and Mapping with a Quadrocopter (J. Sturm, E. Bylow, F. Kahl and D. Cremers), In Unmanned Aerial Vehicle in Geomatics (UAV-g), 2013.  [bibtex] [pdf]
[]Scale-Aware Object Tracking with Convex Shape Constraints on RGB-D Images (M. Klodt, J. Sturm and D. Cremers), In German Conference on Pattern Recognition (GCPR), 2013.  [bibtex] [pdf]
[]FollowMe: Person Following and Gesture Recognition with a Quadrocopter (T. Naseer, J. Sturm and D. Cremers), In Proc. of the Int. Conf. on Intelligent Robot Systems (IROS), 2013.  [bibtex] [pdf]
[]Dense Visual SLAM for RGB-D Cameras (C. Kerl, J. Sturm and D. Cremers), In Proc. of the Int. Conf. on Intelligent Robot Systems (IROS), 2013.  [bibtex] [pdf]
[]Efficient Convex Optimization for Minimal Partition Problems with Volume Constraints (T. Möllenhoff, C. Nieuwenhuis, E. Toeppe and D. Cremers), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2013.  [bibtex] [pdf]
[]Performance Evaluation of Narrow Band Methods for Variational Stereo (F. Stangl, M. Souiai and D. Cremers), In 35th German Conference on Pattern Recognition (GCPR), 2013.  [bibtex] [pdf]
[]A Co-occurrence Prior for Continuous Multi-Label Optimization (M. Souiai, E. Strekalovskiy, C. Nieuwenhuis and D. Cremers), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2013.  [bibtex] [pdf]
[]Direct Camera Pose Tracking and Mapping With Signed Distance Functions (E. Bylow, J. Sturm, C. Kerl, F. Kahl and D. Cremers), In Demo Track of the RGB-D Workshop on Advanced Reasoning with Depth Cameras at the Robotics: Science and Systems Conference (RSS), 2013.  [bibtex] [pdf]
[]Real-Time Camera Tracking and 3D Reconstruction Using Signed Distance Functions (E. Bylow, J. Sturm, C. Kerl, F. Kahl and D. Cremers), In Robotics: Science and Systems Conference (RSS), 2013.  [bibtex] [pdf]
[]Volume Constraints for Single View Reconstruction (E. Toeppe, C. Nieuwenhuis and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.  [bibtex]
[]Robust Odometry Estimation for RGB-D Cameras (C. Kerl, J. Sturm and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2013.  [bibtex] [pdf]Best Vision Paper Award - Finalist
[]Noise Reduction in Accelerated Diffusion Spectrum Imaging through Integration of SENSE Reconstruction into Joint Reconstruction in Combination with q-Space Compressed Sensing (V. Golkov, T. Sprenger, M.I. Menzel, E.T. Tan, K.F. King, C.J. Hardy, L. Marinelli, D. Cremers and J.I. Sperl), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2013.  [bibtex]
[]Corrected Joint SENSE Reconstruction, Low-Rank Constraints, and Compressed-Sensing-Accelerated Diffusion Spectrum Imaging in Denoising and Kurtosis Tensor Estimation (V. Golkov, M.I. Menzel, T. Sprenger, A. Menini, D. Cremers and J.I. Sperl), In ISMRM Workshop on Diffusion as a Probe of Neural Tissue Microstructure, 2013.  [bibtex]
[]Reconstruction, Regularization, and Quality in Diffusion MRI Using the Example of Accelerated Diffusion Spectrum Imaging (V. Golkov, M.I. Menzel, T. Sprenger, A. Menini, D. Cremers and J.I. Sperl), In 16th Annual Meeting of the German Chapter of the ISMRM, 2013.  [bibtex]Oral Presentation
[]Line-Process-Based Joint SENSE Reconstruction of Diffusion Images with Intensity Inhomogeneity Correction and Noise Non-Stationarity Correction (V. Golkov, T. Sprenger, M.I. Menzel, D. Cremers and J.I. Sperl), In European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) Annual Meeting, 2013.  [bibtex]Certificate of Merit Award
[]Effects of Low-Rank Constraints, Line-Process Denoising, and q-Space Compressed Sensing on Diffusion MR Image Reconstruction and Kurtosis Tensor Estimation (V. Golkov, T. Sprenger, A. Menini, M.I. Menzel, D. Cremers and J.I. Sperl), In European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) Annual Meeting, 2013.  [bibtex]Oral Presentation
[]Proximity Priors for Variational Semantic Segmentation and Recognition (J. Bergbauer, C. Nieuwenhuis, M. Souiai and D. Cremers), In ICCV Workshop on Graphical Models for Scene Understanding, 2013.  [bibtex] [doi] [pdf]
[]Convex Optimization for Scene Understanding (M. Souiai, C. Nieuwenhuis, E. Strekalovskiy and D. Cremers), In ICCV Workshop on Graphical Models for Scene Understanding, 2013.  [bibtex] [pdf]
2012
[]Wehrli 2.0: An Algorithm for ”Tidying up Art” (N. Ufer, M. Souiai and D. Cremers), In VISART “Where Computer Vision Meets Art” workshop, ECCV 2012, Springer, 2012.  [bibtex] [pdf]
[]Evaluating Egomotion and Structure-from-Motion Approaches Using the TUM RGB-D Benchmark (J. Sturm, W. Burgard and D. Cremers), In Proc. of the Workshop on Color-Depth Camera Fusion in Robotics at the IEEE/RJS International Conference on Intelligent Robot Systems (IROS), 2012.  [bibtex] [pdf]
[]Accurate Figure Flying with a Quadrocopter Using Onboard Visual and Inertial Sensing (J. Engel, J. Sturm and D. Cremers), In Proc. of the Workshop on Visual Control of Mobile Robots (ViCoMoR) at the IEEE/RJS International Conference on Intelligent Robot Systems (IROS), 2012.  [bibtex] [pdf]
[]A Benchmark for the Evaluation of RGB-D SLAM Systems (J. Sturm, N. Engelhard, F. Endres, W. Burgard and D. Cremers), In Proc. of the International Conference on Intelligent Robot Systems (IROS), 2012.  [bibtex] [pdf]
[]Camera-Based Navigation of a Low-Cost Quadrocopter (J. Engel, J. Sturm and D. Cremers), In Proc. of the International Conference on Intelligent Robot Systems (IROS), 2012.  [bibtex] [pdf] [video]
[]A Convex Representation for the Vectorial Mumford-Shah Functional (E. Strekalovskiy, A. Chambolle and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.  [bibtex] [pdf]
[]Fast and Globally Optimal Single View Reconstruction of Curved Objects (M. R. Oswald, E. Toeppe and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.  [bibtex] [pdf]
[]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] (T. Windheuser, H. Ishikawa and D. Cremers), In Meeting on Image Recognition and Understanding, 2012.  [bibtex] [pdf]
[]Generalized Roof Duality for Multi-Label Optimization: Optimal Lower Bounds and Persistency (T. Windheuser, H. Ishikawa and D. Cremers), In European Conference on Computer Vision (ECCV), 2012.  [bibtex] [pdf]
[]Nonmetric Priors for Continuous Multilabel Optimization (E. Strekalovskiy, C. Nieuwenhuis and D. Cremers), In European Conference on Computer Vision (ECCV), Springer, 2012.  [bibtex] [pdf]
[]Real-Time Human Motion Tracking using Multiple Depth Cameras (L. Zhang, J. Sturm, D. Cremers and D. Lee), In Proc. of the International Conference on Intelligent Robot Systems (IROS), 2012.  [bibtex] [pdf]
[]Box-Particle PHD Filter for Multi-Target Tracking (M. Schikora, A. Gning, L. Mihaylova, D. Cremers and W. Koch), In 15th International Conference on Information Fusion (FUSION), 2012.  [bibtex] [pdf]
[]Box-Particle Intensity Filter (M. Schikora, A. Gning, L. Mihaylova, D. Cremers, W. Koch and R. Streit), In 9th IET Data Fusion and Target Tracking Conference, 2012.  [bibtex] [pdf]
[]A Generalized Framework for Opening Doors and Drawers in Kitchen Environments (T. Ruehr, J. Sturm, D. Pangercic, M. Beetz and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2012.  [bibtex] [pdf]
[]An Evaluation of the RGB-D SLAM System (F. Endres, J. Hess, N. Engelhard, J. Sturm, D. Cremers and W. Burgard), In International Conference on Robotics and Automation (ICRA), 2012.  [bibtex] [pdf]
2011
[]A Survey on Geometry Recovery from a Single Image with Focus on Curved Object Reconstruction (M. R. Oswald, E. Toeppe, C. Nieuwenhuis and D. Cremers), In Proceedings of the 2011 Conference on Innovations for Shape Analysis: Models and Algorithms, Springer-Verlag, 2011.  [bibtex] [pdf]
[]Silhouette-Based Variational Methods for Single View Reconstruction (E. Toeppe, M. R. Oswald, D. Cremers and C. Rother), In Proceedings of the 2010 international conference on Video Processing and Computational Video (D. Cremers, M. A. Magnor, M. R. Oswald, L. Zelnik-Manor, eds.), Springer-Verlag, 2011.  [bibtex] [pdf]
[]Multi-object tracking via high accuracy optical flow and finite set statistics (M. Schikora, W. Koch and D. Cremers), In International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011.  [bibtex] [pdf]
[]Sequential Monte Carlo Method for the iFilter (M. Schikora, W. Koch, R.L. Streit and D. Cremers), In 14th International Conference on Information Fusion (FUSION), 2011.  [bibtex] [pdf]
[]Pixel-based Classification Method for Detecting Unhealthy Regions in Leaf Images (S. Madhogaria, M. Schikora, W. Koch and D. Cremers), In 6th IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2011.  [bibtex] [pdf]
[]Multiple Source Localization Based on Biased Bearings Using the Intensity Filter - Approach and Experimental Results (M. Schikora, M.Oispuu, W. Koch and D. Cremers), In 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2011.  [bibtex] [pdf]
[]Real-Time Visual Odometry from Dense RGB-D Images (F. Steinbruecker, J. Sturm and D. Cremers), In Workshop on Live Dense Reconstruction with Moving Cameras at the Intl. Conf. on Computer Vision (ICCV), 2011.  [bibtex] [pdf]
[]The Wave Kernel Signature: A Quantum Mechanical Approach To Shape Analysis (M. Aubry, U. Schlickewei and D. Cremers), In IEEE International Conference on Computer Vision (ICCV) - Workshop on Dynamic Shape Capture and Analysis (4DMOD), 2011.  [bibtex] [pdf] [code]
[]A Convex Framework for Image Segmentation with Moment Constraints (M. Klodt and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2011.  [bibtex] [pdf]
[]Space-Varying Color Distributions for Interactive Multiregion Segmentation: Discrete versus Continuous Approaches (C. Nieuwenhuis, E. Toeppe and D. Cremers), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2011.  [bibtex] [pdf]
[]Towards a benchmark for RGB-D SLAM evaluation (J. Sturm, S. Magnenat, N. Engelhard, F. Pomerleau, F. Colas, W. Burgard, D. Cremers and R. Siegwart), In Proc. of the RGB-D Workshop on Advanced Reasoning with Depth Cameras at Robotics: Science and Systems Conf. (RSS), 2011.  [bibtex] [pdf] [pdf]
[]Generalized Ordering Constraints for Multilabel Optimization (E. Strekalovskiy and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2011. (oral presentation) [bibtex] [pdf]
[]Decoupling Photometry and Geometry in Dense Variational Camera Calibration (M. Aubry, K. Kolev, B. Goldluecke and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2011.  [bibtex] [pdf]
[]Tight Convex Relaxations for Vector-Valued Labeling Problems (E. Strekalovskiy, B. Goldluecke and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2011.  [bibtex] [pdf]
[]Introducing Total Curvature for Image Processing (B. Goldluecke and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2011.  [bibtex] [pdf]
[]Total Variation for Cyclic Structures: Convex Relaxation and Efficient Minimization (E. Strekalovskiy and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.  [bibtex] [pdf]
[]On a linear programming approach to the discrete Willmore boundary value problem and generalizations (T. Schoenemann, S. Masnou and D. Cremers), In Curves and Surfaces 2011 (J.-D. Boissonnat et al., ed.), 2011.  [bibtex] [pdf]
[]Pose-Consistent 3D Shape Segmentation Based on a Quantum Mechanical Feature Descriptor (M. Aubry, U. Schlickewei and D. Cremers), In Pattern Recognition (Proc. DAGM), Springer, 2011.  [bibtex] [pdf]
[]Geometrically Consistent Elastic Matching of 3D Shapes: A Linear Programming Solution (T. Windheuser, U. Schlickewei, F. R. Schmidt and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2011.  [bibtex] [pdf]
2010
[]Convex Relaxation for Multilabel Problems with Product Label Spaces (B. Goldluecke and D. Cremers), In European Conference on Computer Vision (ECCV), 2010.  [bibtex] [pdf]
[]An Approach to Vectorial Total Variation based on Geometric Measure Theory (B. Goldluecke and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.  [bibtex] [pdf]
[]Parallel Generalized Thresholding Scheme for Live Dense Geometry from a Handheld Camera (J. Stühmer, S. Gumhold and D. Cremers), In ECCV Workshop on Computer Vision on GPUs (CVGPU), 2010.  [bibtex] [pdf]
[]Real-Time Dense Geometry from a Handheld Camera (J. Stühmer, S. Gumhold and D. Cremers), In Pattern Recognition (Proc. DAGM), 2010.  [bibtex] [pdf]
[]Anisotropic Minimal Surfaces Integrating Photoconsistency and Normal Information for Multiview Stereo (K. Kolev, T. Pock and D. Cremers), In European Conference on Computer Vision (ECCV), 2010.  [bibtex] [pdf]
[]Image-based 3D Modeling via Cheeger Sets (E. Toeppe, M. R. Oswald, D. Cremers and C. Rother), In Asian Conference on Computer Vision, 2010.  [bibtex] [pdf]Received Honorable Mention Award
[]Multi-target multi-sensor localization and tracking using passive antenna and optical sensors on UAVs (M. Schikora, D. Bender, W. Koch and D. Cremers), In SPIE Security + Defence, 2010.  [bibtex] [pdf]
[]Passive Multi-Object Localization and Tracking Using Bearing Data (M. Schikora, D. Bender, D. Cremers and W. Koch), In 13th International Conference on Information Fusion (FUSION), 2010.  [bibtex] [pdf]
[]Probabilistic Classification of Disease Symptoms caused by Salmonella on Arabidopsis Plants (M. Schikora, A. Schikora, K.-H. Kogel, W. Koch and D. Cremers), In 5th IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2010.  [bibtex] [pdf]
2009
[]Advanced Data Terms for Variational Optic Flow Estimation (F. Steinbruecker, T. Pock and D. Cremers), In Proceedings Vision, Modeling and Visualization (VMV), 2009.  [bibtex] [pdf]
[]Video Super Resolution using Duality Based TV-L1 Optical Flow (D. Mitzel, T. Pock, T. Schoenemann and D. Cremers), In Pattern Recognition (Proc. DAGM), 2009.  [bibtex] [pdf]
[]Large Displacement Optical Flow Computation without Warping (F. Steinbruecker, T. Pock and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2009.  [bibtex] [pdf]
[]Beyond Connecting the Dots: A Polynomial-time Algorithm for Segmentation and Boundary Estimation with Imprecise User Input (T. Windheuser, T. Schoenemann and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2009.  [bibtex] [pdf]
[]Curvature Regularity for Region-based Image Segmentation and Inpainting: A Linear Programming Relaxation (T. Schoenemann, F. Kahl and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2009.  [bibtex] [pdf]
[]Structure- and Motion-adaptive Regularization for High Accuracy Optic Flow (A. Wedel, D. Cremers, T. Pock and H. Bischof), In IEEE International Conference on Computer Vision (ICCV), 2009.  [bibtex] [pdf]
[]An Algorithm for Minimizing the Piecewise Smooth Mumford-Shah Functional (T. Pock, D. Cremers, H. Bischof and A. Chambolle), In IEEE International Conference on Computer Vision (ICCV), 2009.  [bibtex] [pdf]
[]Variational Optical Flow from Alternate Exposure Images (A. Sellent, M. Eisemann, B. Goldluecke, T. Pock, D. Cremers and M. Magnor), In Proceedings Vision, Modeling and Visualization (VMV), 2009.  [bibtex] [pdf]
[]Superresolution Texture Maps for Multiview Reconstruction (B. Goldluecke and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2009.  [bibtex] [pdf]
[]A Superresolution Framework for High-Accuracy Multiview Reconstruction (B. Goldluecke and D. Cremers), In Pattern Recognition (Proc. DAGM), 2009.  [bibtex] [pdf]Received DAGM Best Paper Award
[]Detection and Segmentation of Independently Moving Objects from Dense Scene Flow (A. Wedel, C. Rabe, A. Meissner, U. Franke and D. Cremers), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR) (D. Cremers, Y. Boykov, A. Blake, F. R. Schmidt, eds.), volume 5681, 2009.  [bibtex] [pdf]
[]Continuous Ratio Optimization via Convex Relaxation with Applications to Multiview 3D Reconstruction (K. Kolev and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009.  [bibtex] [pdf]
[]A Convex Relaxation Approach for Computing Minimal Partitions (T. Pock, A. Chambolle, H. Bischof and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009.  [bibtex] [pdf]
[]Efficient Planar Graph Cuts with Applications in Computer Vision (F. R. Schmidt, E. Toeppe and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009. (Note: This version of the paper is modified with respect to the published one: In the published version we had only cited the PhD thesis [2], as the conference paper [3] does not mention Step 13 of Algorithm 2 that restores the important property of T being a rooted tree.) [bibtex] [pdf]Received a CVPR Doctoral Spotlight Award
[]A Closed-Form Solution for Image Sequence Segmentation with Dynamical Shape Priors (F. R. Schmidt and D. Cremers), In Pattern Recognition (Proc. DAGM), 2009.  [bibtex] [pdf]
[]Non-Parametric Single View Reconstruction of Curved Objects using Convex Optimization (M. R. Oswald, E. Toeppe, K. Kolev and D. Cremers), In Pattern Recognition (Proc. DAGM), 2009.  [bibtex] [pdf]Received a DAGM Paper Award
2008
[]Nonlocal texture filtering with efficient tree structures and invariant patch similarity measures (O. Kleinschmidt, T. Brox and D. Cremers), In Int. Workshop on Local and Nonlocal Approximation, 2008.  [bibtex] [pdf]
[]Modeling and Tracking Line-Constrained Mechanical Systems (B. Rosenhahn, T. Brox, D. Cremers and H.-P. Seidel), In 2nd Workshop on Robot Vision (G. Sommer, R. Klette, eds.), volume 4931, 2008.  [bibtex] [pdf]
[]High Resolution Motion Layer Decomposition using Dual-space Graph Cuts (T. Schoenemann and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.  [bibtex] [pdf]
[]Globally Optimal Shape-based Tracking in Real-time (T. Schoenemann and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.  [bibtex] [pdf]
[]Matching Non-rigidly Deformable Shapes Across Images: A Globally Optimal Solution (T. Schoenemann and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.  [bibtex] [pdf]
[]Markerless Motion Capture of Man-Machine Interaction (B. Rosenhahn, C. Schmaltz, T. Brox, J. Weickert, D. Cremers and H.-P. Seidel), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.  [bibtex] [pdf]
[]Shape Priors in Variational Image Segmentation: Convexity, Lipschitz Continuity and Globally Optimal Solutions (D. Cremers, F. R. Schmidt and F. Barthel), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.  [bibtex] [pdf]
[]An Unbiased Second-Order Prior for High-Accuracy Motion Estimation (W. Trobin, T. Pock, D. Cremers and H. Bischof), In Pattern Recognition (Proc. DAGM), Springer, 2008.  [bibtex] [pdf]
[]An Improved Algorithm for TV-L1 Optical Flow (A. Wedel, T. Pock, C. Zach, D. Cremers and H. Bischof), In Proc. of the Dagstuhl Motion Workshop, Springer, 2008.  [bibtex] [pdf]
[]Fast and Exact Solution of Total Variation Models on the GPU (T. Pock, M. Unger, D. Cremers and H. Bischof), In CVPR Workshop on Visual Computer Vision on GPU's, 2008.  [bibtex] [pdf]
[]An Experimental Comparison of Discrete and Continuous Shape Optimization Methods (M. Klodt, T. Schoenemann, K. Kolev, M. Schikora and D. Cremers), In European Conference on Computer Vision (ECCV), 2008.  [bibtex] [pdf]
[]Duality TV-L1 Flow with Fundamental Matrix Prior (A. Wedel, T. Pock, J. Braun, U. Franke and D. Cremers), In Image Vision and Computing, 2008.  [bibtex] [pdf]
[]Efficient Dense Scene Flow from Sparse or Dense Stereo Data (A. Wedel, C. Rabe, T. Vaudrey, T. Brox, U. Franke and D. Cremers), In European Conference on Computer Vision (ECCV), 2008.  [bibtex] [pdf]
[]Integration of Multiview Stereo and Silhouettes via Convex Functionals on Convex Domains (K. Kolev and D. Cremers), In European Conference on Computer Vision (ECCV), 2008.  [bibtex] [pdf]
[]Continuous Energy Minimization via Repeated Binary Fusion (W. Trobin, T. Pock, D. Cremers and H. Bischof), In European Conference on Computer Vision (ECCV), 2008.  [bibtex] [pdf]
[]A Convex Formulation of Continuous Multi-Label Problems (T. Pock, T. Schoenemann, G. Graber, H. Bischof and D. Cremers), In European Conference on Computer Vision (ECCV), 2008.  [bibtex] [pdf]
[]TVSeg - Interactive Total Variation Based Image Segmentation (M. Unger, T. Pock, D. Cremers and H. Bischof), In British Machine Vision Conference (BMVC), 2008.  [bibtex] [pdf]
[]Image Segmentation with Elastic Shape Priors via Global Geodesics in Product Spaces (T. Schoenemann, F. R. Schmidt and D. Cremers), In British Machine Vision Conference (BMVC), 2008.  [bibtex] [pdf]
2007
[]A probabilistic level set formulation for interactive organ segmentation (D. Cremers, O. Fluck, M. Rousson and S. Aharon), In Proc. of the SPIE Medical Imaging, 2007.  [bibtex] [pdf]
[]Iterated Nonlocal Means for Texture Restoration (T. Brox and D. Cremers), In Proc. International Conference on Scale Space and Variational Methods in Computer Vision (F. Sgallari, A. Murli, N. Paragios, eds.), Springer, volume 4485, 2007.  [bibtex] [pdf]
[]On the Statistical Interpretation of the Piecewise Smooth Mumford-Shah Functional (T. Brox and D. Cremers), In Proc. International Conference on Scale Space and Variational Methods in Computer Vision (F. Sgallari, A. Murli, N. Paragios, eds.), Springer, volume 4485, 2007.  [bibtex] [pdf]
[]Nonlinear Dynamical Shape Priors for Level Set Segmentation (D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007.  [bibtex] [pdf]
[]Efficient Shape Matching via Graph Cuts (F. R. Schmidt, E. Toeppe, D. Cremers and Y. Boykov), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Springer, volume 4679, 2007.  [bibtex] [pdf]
[]Online smoothing for markerless motion capture (B. Rosenhahn, T. Brox, D. Cremers and H.-P. Seidel), In Pattern Recognition (Proc. DAGM), Springer, 2007.  [bibtex] [pdf]
[]WarpCut - Fast obstacle segmentation in monocular video (A. Wedel, T. Schoenemann, T. Brox and D. Cremers), In Pattern Recognition (Proc. DAGM), Springer, 2007.  [bibtex] [pdf]
[]Intrinsic Mean for Semimetrical Shape Retrieval via Graph Cuts (F. R. Schmidt, E. Toeppe, D. Cremers and Y. Boykov), In Pattern Recognition (Proc. DAGM), Springer, volume 4713, 2007.  [bibtex] [pdf]
[]Fast Matching of Planar Shapes in Sub-cubic Runtime (F. R. Schmidt, Dirk Farin and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2007.  [bibtex] [pdf]
[]Introducing Curvature into Globally Optimal Image Segmentation: Minimum Ratio Cycles on Product Graphs (T. Schoenemann and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2007. (After publication, we became aware that a similar graph representation was proposed in a shortest-path context by Amini et al., PAMI 1990.) [bibtex] [pdf]
[]Globally Optimal Image Segmentation with an Elastic Shape Prior (T. Schoenemann and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2007.  [bibtex] [pdf]
[]Nonparametric density estimation with adaptive anisotropic kernels for human motion tracking (T. Brox, B. Rosenhahn, D. Cremers and H.-P. Seidel), In Proc. 2nd International Workshop on Human Motion (A. Elgammal, B. Rosenhahn, R. Klette, eds.), Springer, volume 4814, 2007.  [bibtex] [pdf]
[]Continuous Global Optimization in Multiview 3D Reconstruction (K. Kolev, M. Klodt, T. Brox, S. Esedoglu and D. Cremers), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Springer, volume 4679, 2007.  [bibtex] [pdf]
[]Propagated Photoconsistency and Convexity in Variational Multiview 3D Reconstruction (K. Kolev, M. Klodt, T. Brox and D. Cremers), In Workshop on Photometric Analysis for Computer Vision, 2007.  [bibtex] [pdf]
2006
[]4D shape priors for level set segmentation of the left myocardium in SPECT sequences (T. Kohlberger, D. Cremers, M. Rousson and R. Ramaraj), In Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 4190, 2006.  [bibtex] [pdf]
[]GPU histogram computation (O. Fluck, S. Aharon, D. Cremers and M. Rousson), In ACM SIGGRAPH posters and demos, 2006.  [bibtex] [pdf]
[]Nonparametric priors on the space of joint intensity distributions for non-rigid multi-modal image registration (D. Cremers, C. Guetter and C. Xu), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 2, 2006.  [bibtex] [pdf]
[]Statistical priors for combinatorial optimization: efficient solutions via Graph Cuts (D. Cremers and L. Grady), In European Conference on Computer Vision (ECCV) (A. Leonardis, H. Bischof, A. Pinz, eds.), Springer, volume 3953, 2006.  [bibtex] [pdf]
[]High accuracy optical flow serves 3-D pose tracking: exploiting contour and flow based constraints (T. Brox, B. Rosenhahn, D. Cremers and H.-P. Seidel), In European Conference on Computer Vision (ECCV) (A. Leonardis, H. Bischof, A. Pinz, eds.), Springer, volume 3952, 2006.  [bibtex] [pdf]
[]A comparison of shape matching methods for contour based pose estimation (B. Rosenhahn, T. Brox, D. Cremers and H.-P. Seidel), In Proc. International Workshop on Combinatorial Image Analysis (R. Reulke, U. Eckhardt, B. Flach, U. Knauer, K. Polthier, eds.), Springer, volume 4040, 2006.  [bibtex] [pdf]
[]An integral solution to surface evolution PDEs via Geo-Cuts (Y. Boykov, V. Kolmogorov, D. Cremers and A. Delong), In European Conference on Computer Vision (ECCV) (A. Leonardis, H. Bischof, A. Pinz, eds.), Springer, volume 3953, 2006.  [bibtex] [pdf]
[]Realtime depth estimation and obstacle detection from monocular video (A. Wedel, U. Franke, J. Klappstein, T. Brox and D. Cremers), In Pattern Recognition (Proc. DAGM) (K. Franke et al., ed.), Springer, volume 4174, 2006.  [bibtex] [pdf]
[]Robust variational segmentation of 3D objects from multiple views (K. Kolev, T. Brox and D. Cremers), In Pattern Recognition (Proc. DAGM) (K. Franke et al., ed.), Springer, volume 4174, 2006.  [bibtex] [pdf]
[]Nonparametric density estimation for human pose tracking (T. Brox, B. Rosenhahn, U. Kersting and D. Cremers), In Pattern Recognition (Proc. DAGM) (K. Franke et al., ed.), Springer, volume 4174, 2006.  [bibtex] [pdf]
[]Near Real-time Motion Segmentation using Graph Cuts (T. Schoenemann and D. Cremers), In Pattern Recognition (Proc. DAGM), Springer, volume 4174, 2006.  [bibtex] [pdf]
[]Shape Matching by Variational Computation of Geodesics on a Manifold (F. R. Schmidt, M. Clausen and D. Cremers), In Pattern Recognition (Proc. DAGM), Springer, volume 4174, 2006.  [bibtex] [pdf]
2005
[]Efficient kernel density estimation of shape and intensity priors for level set segmentation (M. Rousson and D. Cremers), In Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 1, 2005.  [bibtex] [pdf]
[]One-shot integral invariant shape priors for variational segmentation (S. Manay, D. Cremers, A. J. Yezzi and S. Soatto), In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR) (A. Rangarajan, B. Vemuri, A. L. Yuille, eds.), volume 3757, 2005.  [bibtex]
[]Dynamical statistical shape priors for level set based tracking (D. Cremers and G. Funka-Lea), In Intl. Workshop on Variational and Level Set Methods (N. Paragios, F. Faugeras, T. Chan, C. Schnörr, eds.), Springer, volume 3752, 2005. (210–221) [bibtex] [pdf]
2004
[]Shedding light on stereoscopic segmentation (H. Jin, D. Cremers, A. Yezzi and S. Soatto), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (L. Davis, ed.), volume 1, 2004.  [bibtex] [pdf]
[]Multiphase dynamic labeling for variational recognition-driven image segmentation (D. Cremers, N. Sochen and C. Schnörr), In European Conference on Computer Vision (ECCV) (T. Pajdla, V. Hlavac, eds.), Springer, volume 3024, 2004.  [bibtex] [pdf]
[]Kernel density estimation and intrinsic alignment for knowledge-driven segmentation: Teaching level sets to walk (D. Cremers, S. J. Osher and S. Soatto), In Pattern Recognition (Proc. DAGM) (C. E. Rasmussen, ed.), Springer, volume 3175, 2004.  [bibtex] [pdf]
[]Bayesian Approaches to Motion-based Image and Video Segmentation (D. Cremers), In 1st Int. Workshop on Complex Motion, Springer, volume 3417, 2004.  [bibtex] [pdf]
2003
[]Dynamic texture segmentation (G. Doretto, D. Cremers, P. Favaro and S. Soatto), In IEEE International Conference on Computer Vision (ICCV) (B. Triggs, A. Zisserman, eds.), volume 2, 2003.  [bibtex] [pdf]
[]A generative model based approach to motion segmentation (D. Cremers and A. L. Yuille), In Pattern Recognition (Proc. DAGM) (B. Michaelis, G. Krell, eds.), Springer, volume 2781, 2003.  [bibtex] [pdf]
[]Towards Recognition-based Variational Segmentation Using Shape Priors and Dynamic Labeling (D. Cremers, N. Sochen and C. Schnörr), In Scale-Space Methods in Computer Vision (L. D. Griffin, M. Lillholm, eds.), Springer, volume 2695, 2003.  [bibtex] [pdf]
[]Variational space-time motion segmentation (D. Cremers and S. Soatto), In IEEE International Conference on Computer Vision (ICCV) (B. Triggs, A. Zisserman, eds.), volume 2, 2003.  [bibtex] [pdf]
[]A pseudo-distance for shape priors in level set segmentation (D. Cremers and S. Soatto), In IEEE 2nd Int. Workshop on Variational, Geometric and Level Set Methods (N. Paragios, ed.), 2003.  [bibtex] [pdf]
[]A multiphase level set framework for variational motion segmentation (D. Cremers), In Scale-Space Methods in Computer Vision (L. D. Griffin, M. Lillholm, eds.), Springer, volume 2695, 2003.  [bibtex] [pdf]
[]A variational framework for image segmentation combining motion estimation and shape regularization (D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (C. Dyer, P. Perona, eds.), volume 1, 2003.  [bibtex] [pdf]
2002
[]Statistical shape knowledge in variational motion segmentation (D. Cremers and C. Schnörr), In 1st Internat. Workshop on Generative-Model-Based Vision (A. Pece, Y. N. Wu, R. Larsen, eds.), Univ. of Copenhagen, 2002. (http://www.diku.dk/research/published/2002/02-01) [bibtex]
[]Motion Competition: variational integration of motion segmentation and shape regularization (D. Cremers and C. Schnörr), In Pattern Recognition (Proc. DAGM) (L. van Gool, ed.), Springer, volume 2449, 2002.  [bibtex] [pdf]Received the Best Paper Award
[]Nonlinear shape statistics in Mumford–Shah based segmentation (D. Cremers, T. Kohlberger and C. Schnörr), In European Conference on Computer Vision (ECCV) (A. Heyden, others, eds.), Springer, volume 2351, 2002.  [bibtex] [pdf]
[]Unsupervised Image Partitioning with Semidefinite Programmifng (J. Keuchel, C. Schnoerr, C. Schellewald and D. Cremers), In Pattern Recognition (van Gool, L., ed.), Springer, volume 2449, 2002.  [bibtex]
2001
[]Diffusion Snakes: Combining statistical shape knowledge and image information in a variational framework (D. Cremers, C. Schnörr and J. Weickert), In IEEE First Int. Workshop on Variational and Level Set Methods (N. Paragios, ed.), 2001.  [bibtex]Best Student Paper Award
[]Convex Relaxations for Binary Image Partitioning and Perceptual Grouping (J. Keuchel, C. Schellewald, D. Cremers and C. Schnoerr), In Pattern Recognition (Radig, B., Florczyk, S., eds.), Springer, volume 2191, 2001.  [bibtex]Received a DAGM Paper Award
[]Nonlinear shape statistics via kernel spaces (D. Cremers, T. Kohlberger and C. Schnörr), In Pattern Recognition (Proc. DAGM) (B. Radig, S. Florczyk, eds.), Springer, volume 2191, 2001.  [bibtex] [pdf]
2000
[]Diffusion Snakes using statistical shape knowledge (D. Cremers, C. Schnörr, J. Weickert and C. Schellewald), In Algebraic Frames for the Perception-Action Cycle (C. Sommer, Y.Y. Zeevi, eds.), Springer, volume 1888, 2000.  [bibtex]
[]Learning of translation invariant shape knowledge for steering diffusion snakes (D. Cremers, C. Schnörr, J. Weickert and C. Schellewald), In Dynamische Perzeption (G. Baratoff, H. Neumann, eds.), Infix, volume 9, 2000.  [bibtex]
Books Book Chapters Journal Articles Preprints Conference and Workshop Papers PhD Thesis Technical Reports inproceedings 
PhD Thesis
2002
[]Statistical shape knowledge in variational image segmentation (D. Cremers), PhD thesis, Department of Mathematics and Computer Science, University of Mannheim, Germany, 2002.  [bibtex] [pdf]
Books Book Chapters Journal Articles Preprints Conference and Workshop Papers PhD Thesis Technical Reports inproceedings 
Technical Reports
2013
[]Label Configuration Priors for Continuous Multi-Label Optimization (M. Souiai, E. Strekalovskiy, C. Nieuwenhuis and D. Cremers), Technical report, , 2013.  [bibtex] [pdf]
2008
[]A Convex Approach for Computing Minimal Partitions (A. Chambolle, D. Cremers and T. Pock), Technical report, Dept. of Computer Science, University of Bonn, 2008.  [bibtex] [pdf]
2007
[]Iterated and Efficient Nonlocal Means for Denoising of Textural Patterns (T. Brox, O. Kleinschmidt and D. Cremers), Technical report, Dept. of Computer Science, University of Bonn, 2007. (Available by e-mail on request.) [bibtex]
2000
[]Diffusion Snakes using statistical shape knowledge (D. Cremers, C. Schnörr, J. Weickert and C. Schellewald), Technical report, Dept. of Math. and Comp. Sci., Comp. Sci. Series, 2000.  [bibtex]
Books Book Chapters Journal Articles Preprints Conference and Workshop Papers PhD Thesis Technical Reports inproceedings 
inproceedings
2007
[]Region-based Pose Tracking (C. Schmaltz, B. Rosenhahn, T. Brox, D. Cremers, J. Weickert, L. Wietzke and G. Sommer), In Proc. 3rd Iberian Conference on Pattern Recognition and Image Analysis, Springer, 2007.  [bibtex] [pdf]
[]Occlusion Modeling by Tracking Multiple Objects (C. Schmaltz, B. Rosenhahn, T. Brox, D. Cremers, J. Weickert, L. Wietzke and G. Sommer), In Pattern Recognition (Proc. DAGM), Springer, 2007.  [bibtex] [pdf]
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