Occlusion detection in dense stereo estimation with convex optimization

In this paper, we propose a dense two-frame stereo algorithm which handles occlusion in a variational framework. Our method is based on a new regularization model which includes both a constraint on the occlusion width and a visibility constraint in nonoccluded areas. The minimization of the resulting energy functional is done by convex relaxation. A post-processing then detects and fills the occluded regions. We also propose a novel dissimilarity measure that combines color and gradient comparison with a variable respective weight, to benefit from the robustness of the comparison based on local variations while avoiding the fattening effect it may generate.