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

Yvain Quéau (1) , Tao Wu (1) , Jean-Denis Durou (2) , François Lauze (3) , Daniel Cremers (1)

1 TUM - Technische Universität Munchen - Technical University Munich - Université Technique de Munich
2 IRIT-REVA - Real Expression Artificial Life
3 UCPH - University of Copenhagen = Københavns Universitet

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.

Mots clés: Cameras; Random variables; Computational modeling; Lighting; Robustness; Mathematical model

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]; Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

Yvain Quéau, Tao Wu, Jean-Denis Durou, François Lauze, Daniel Cremers. A Non-Convex Variational Approach to Photometric Stereo under Inaccurate Lighting. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul 2017, Honolulu, United States. pp. 99-108, ⟨10.1109/CVPR.2017.45⟩. ⟨hal-01809278⟩