A non-convex variational approach to photometric stereo under inaccurate lighting

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

A non-convex variational approach to photometric stereo under inaccurate lighting. / Quéau, Yvain; Wu, Tao; Lauze, Francois Bernard; Durou, Jean-Denis; Cremers, Daniel.

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. p. 350-359.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Quéau, Y, Wu, T, Lauze, FB, Durou, J-D & Cremers, D 2017, A non-convex variational approach to photometric stereo under inaccurate lighting. in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 350-359, 2017 IEEE Conference on Computer Vision and Pattern Recognition , Honolulu, Hawaii, United States, 21/07/2017. https://doi.org/10.1109/CVPR.2017.45

APA

Quéau, Y., Wu, T., Lauze, F. B., Durou, J-D., & Cremers, D. (2017). A non-convex variational approach to photometric stereo under inaccurate lighting. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 350-359). IEEE. https://doi.org/10.1109/CVPR.2017.45

Vancouver

Quéau Y, Wu T, Lauze FB, Durou J-D, Cremers D. A non-convex variational approach to photometric stereo under inaccurate lighting. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. 2017. p. 350-359 https://doi.org/10.1109/CVPR.2017.45

Author

Quéau, Yvain ; Wu, Tao ; Lauze, Francois Bernard ; Durou, Jean-Denis ; Cremers, Daniel. / A non-convex variational approach to photometric stereo under inaccurate lighting. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. pp. 350-359

Bibtex

@inproceedings{394140e4e62e430eb97a58eb08d08053,
title = "A non-convex variational approach to photometric stereo under inaccurate lighting",
abstract = "This paper tackles the photometric stereo problem in the presence of inaccurate lighting, obtained either by calibration or by an uncalibrated photometric stereo method. Based on a precise modeling of noise and outliers, a robust variational approach is introduced. It explicitly accounts for self-shadows, and enforces robustness to cast-shadows and specularities by resorting to redescending M-estimators. The resulting non-convex model is solved by means of a computationally efficient alternating reweighted least-squares algorithm. Since it implicitly enforces integrability, the new variational approach can refine both the intensities and the directions of the lighting.",
author = "Yvain Qu{\'e}au and Tao Wu and Lauze, {Francois Bernard} and Jean-Denis Durou and Daniel Cremers",
year = "2017",
month = jul,
doi = "10.1109/CVPR.2017.45",
language = "English",
pages = "350--359",
booktitle = "2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
publisher = "IEEE",
note = "2017 IEEE Conference on Computer Vision and Pattern Recognition , CVPR 2017 ; Conference date: 21-07-2017 Through 26-07-2017",

}

RIS

TY - GEN

T1 - A non-convex variational approach to photometric stereo under inaccurate lighting

AU - Quéau, Yvain

AU - Wu, Tao

AU - Lauze, Francois Bernard

AU - Durou, Jean-Denis

AU - Cremers, Daniel

PY - 2017/7

Y1 - 2017/7

N2 - This paper tackles the photometric stereo problem in the presence of inaccurate lighting, obtained either by calibration or by an uncalibrated photometric stereo method. Based on a precise modeling of noise and outliers, a robust variational approach is introduced. It explicitly accounts for self-shadows, and enforces robustness to cast-shadows and specularities by resorting to redescending M-estimators. The resulting non-convex model is solved by means of a computationally efficient alternating reweighted least-squares algorithm. Since it implicitly enforces integrability, the new variational approach can refine both the intensities and the directions of the lighting.

AB - This paper tackles the photometric stereo problem in the presence of inaccurate lighting, obtained either by calibration or by an uncalibrated photometric stereo method. Based on a precise modeling of noise and outliers, a robust variational approach is introduced. It explicitly accounts for self-shadows, and enforces robustness to cast-shadows and specularities by resorting to redescending M-estimators. The resulting non-convex model is solved by means of a computationally efficient alternating reweighted least-squares algorithm. Since it implicitly enforces integrability, the new variational approach can refine both the intensities and the directions of the lighting.

U2 - 10.1109/CVPR.2017.45

DO - 10.1109/CVPR.2017.45

M3 - Article in proceedings

SP - 350

EP - 359

BT - 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

PB - IEEE

T2 - 2017 IEEE Conference on Computer Vision and Pattern Recognition

Y2 - 21 July 2017 through 26 July 2017

ER -

ID: 183735717