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

Yvain Quéau; Tao Wu; Francois Bernard 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, Francois Bernard Lauze, Jean-Denis Durou, Daniel Cremers

Department of Computer Science

28 Citations (Scopus)

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.

Original language	English
Title of host publication	2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Number of pages	10
Publisher	IEEE
Publication date	6 Nov 2017
Pages	350-359
ISBN (Electronic)	978-1-5386-0457-1
DOIs	https://doi.org/10.1109/CVPR.2017.45
Publication status	Published - 6 Nov 2017
Event	2017 IEEE Conference on Computer Vision and Pattern Recognition - Hawaii Convention Center, Honolulu, United States Duration: 21 Jul 2017 → 26 Jul 2017

Conference

Conference	2017 IEEE Conference on Computer Vision and Pattern Recognition
Location	Hawaii Convention Center
Country/Territory	United States
City	Honolulu
Period	21/07/2017 → 26/07/2017

Access to Document

10.1109/CVPR.2017.45

Cite this

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

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