Estimating depth through the fusion of photometric stereo images

Abstract

Here we revisit a recently introduced process of shape estimation through the matching of photometric stereo images, which are monocular images obtained under different illuminations. By considering the general solution of the differential equation which relates surface depth to the disparity map produced by the matching process, we are able to obtain a more consistent formulation than previously for such disparity-based approach to photometric stereo. We also employ a simple least-squares regression in a calibration strategy for estimating the parameters required by this approach. Finally, we introduce a multiscale matching procedure, based on a new stochastic metaheuristic for combinatorial optimization, which yields more reliable disparity maps in shorter processing times.