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

Yvain Quéau; Tao Wu; François Lauze; Jean Denis Durou; Daniel Cremers

doi:10.1109/CVPR.2017.45

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

Yvain Quéau, Tao Wu, François Lauze, Jean Denis Durou, Daniel Cremers

Informatics 9 - Chair of Computer Vision and Artificial Intelligence

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

48 Scopus citations

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 castshadows and specularities by resorting to redescending Mestimators. 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.

Original language	English
Title of host publication	Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	350-359
Number of pages	10
ISBN (Electronic)	9781538604571
DOIs	https://doi.org/10.1109/CVPR.2017.45
State	Published - 6 Nov 2017
Event	30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Honolulu, United States Duration: 21 Jul 2017 → 26 Jul 2017

Publication series

Name	Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Volume	2017-January

Conference

Conference	30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Country/Territory	United States
City	Honolulu
Period	21/07/17 → 26/07/17

Access to Document

10.1109/CVPR.2017.45

Cite this

Quéau, Y., Wu, T., Lauze, F., Durou, J. D., & Cremers, D. (2017). A non-convex variational approach to photometric stereo under inaccurate lighting. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 (pp. 350-359). (Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CVPR.2017.45

Quéau, Yvain ; Wu, Tao ; Lauze, François et al. / A non-convex variational approach to photometric stereo under inaccurate lighting. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 350-359 (Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017).

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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 castshadows and specularities by resorting to redescending Mestimators. 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 Fran{\c c}ois Lauze and Durou, {Jean Denis} and Daniel Cremers",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 ; Conference date: 21-07-2017 Through 26-07-2017",

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Quéau, Y, Wu, T, Lauze, F, Durou, JD & Cremers, D 2017, A non-convex variational approach to photometric stereo under inaccurate lighting. in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 350-359, 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, United States, 21/07/17. https://doi.org/10.1109/CVPR.2017.45

A non-convex variational approach to photometric stereo under inaccurate lighting. / Quéau, Yvain; Wu, Tao; Lauze, François et al.
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 350-359 (Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017; Vol. 2017-January).

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

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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 castshadows and specularities by resorting to redescending Mestimators. 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 castshadows and specularities by resorting to redescending Mestimators. 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.

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M3 - Conference contribution

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Quéau Y, Wu T, Lauze F, Durou JD, Cremers D. A non-convex variational approach to photometric stereo under inaccurate lighting. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 350-359. (Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017). doi: 10.1109/CVPR.2017.45

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

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