Occlusion-Aided Weights for Local Stereo Matching

Recently, local stereo matching has experienced large progress by the introduction of adaptive support-weights. In this paper, we aim at eliminating negative effects of occlusions by proposing an occlusion-based method to improve traditional support weights. Weights of occluded points are greatly reduced while computing matching costs, initial disparities and final disparities. Experimental results on the Middlebury images demonstrate that our method is very effective in improving disparities of points around occluded areas and depth discontinuities. According to the Middlebury benchmark, the proposed algorithm is now the top performer among local stereo methods. Moreover, this approach can be easily integrated into nearly all existing support weights strategies.