Consensus multi-view photometric stereo

We propose a multi-view photometric stereo technique that uses photometric normal consistency to jointly estimate surface position and orientation. The underlying scene representation is based on oriented points, yielding more flexibility compared to smoothly varying surfaces. We demonstrate that the often employed least squares error of the Lambertian image formation model fails for wide-baseline settings without known visibility information. We then introduce a multi-view normal consistency approach and demonstrate its efficiency on synthetic and real data. In particular, our approach is able to handle occlusion, shadows, and other sources of outliers.