Occlusion disparity refinement for stereo matching through the geometric prior-based adaptive label search

In stereo matching, occlusion disparity refinement is one of the challenges when attempting to improve disparity accuracy. In order to refine the disparity in occluded regions, a geometric prior guided adaptive label search method and sequential disparity filling strategy are proposed. In our method, considering the scene structural correlation between pixels, the geometric prior information such as image patch similarity, matching distance, and disparity constraint is used in the proposed label search energy function and the disparity labels are searched by superpixel matching. Thus, the reliable disparity labels are adaptively searched and propagated for occlusion filling. In order to improve the accuracy in large occluded regions, by using the proposed sequential filling strategy, occluded regions are decomposed into multiple blocks and filled in multiple steps from the periphery; thus, reliable labels are iteratively propagated to the interior of occluded regions without violating the smooth disparity assumption. Experimental results on the Middlebury V3 benchmark show that, compared with other state-of-the-art algorithms, the proposed method achieves better disparity results under multiple criteria. The proposed method can provide better disparity refinement for typical stereo matching algorithms.