A non-convex variational approach to photometric stereo under inaccurate lighting - Queau, Yvain; Wu, Tao; Lauze, Francois; Durou, Jean-Denis; Cremers, Daniel (Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017)
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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