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

Yvain Queau; Tao Wu; Francois Lauze; Jean-Denis Durou; Daniel Cremers

CVPR 2017poster73 citations

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

Yvain Queau, Tao Wu, Francois Lauze, Jean-Denis Durou, Daniel Cremers

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.

BibTeX

@inproceedings{cvpr2017_anonconvexvariat,
  title = {A Non-Convex Variational Approach to Photometric Stereo Under Inaccurate Lighting},
  author = {Yvain Queau and Tao Wu and Francois Lauze and Jean-Denis Durou and Daniel Cremers},
  booktitle = {CVPR 2017},
  year = {2017}
}