A Dense Stereo Matching Algorithm with Occlusion and Less or Similar Texture Handling

Abstract

Due to image noise, illumination and occlusion, to get an accurate and dense disparity with stereo matching is still a challenge. In this paper, a new dense stereo matching algorithm is proposed. The proposed algorithm first use cross-based regions to compute an initial disparity map which can deal with regions with less or similar texture. Secondly, the improved hierarchical belief propagation scheme is employed to optimize the initial disparity map. Then the left-right consistency check and mean-shift algorithm are used to handle occlusions. Finally, a local high-confidence strategy is used to refine the disparity map. Experiments with the Middlebury dataset validate the proposed algorithm.