A Non-Convex Variational Approach to Photometric Stereo Under Inaccurate Lighting

Yvain Queau, Tao Wu, Francois Lauze, Jean-Denis Durou, Daniel Cremers; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 99-108

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.

Related Material


[pdf]
[bibtex]
@InProceedings{Queau_2017_CVPR,
author = {Queau, Yvain and Wu, Tao and Lauze, Francois and Durou, Jean-Denis and Cremers, Daniel},
title = {A Non-Convex Variational Approach to Photometric Stereo Under Inaccurate Lighting},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}