Unbiased Photometric Stereo for Colored Surfaces: A Variational Approach

Yvain Queau, Roberto Mecca, Jean-Denis Durou; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 4359-4368

3D shape recovery using photometric stereo (PS) gained increasing attention in the computer vision community in the last three decades due to its ability to recover the thinnest geometric structures. Yet, the reliabiliy of PS for color images is difficult to guarantee, because existing methods are usually formulated as the sequential estimation of the colored albedos, the normals and the depth. Hence, the overall reliability depends on that of each subtask. In this work we propose a new formulation of color photometric stereo, based on image ratios, that makes the technique independent from the albedos. This allows the unbiased 3D-reconstruction of colored surfaces in a single step, by solving a system of linear PDEs using a variational approach.