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