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@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},
}