% generated by bibtexbrowser % % Encoding: UTF-8 @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{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}, } @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{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}, } @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}, } @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}, } @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{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{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{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}, }