Occlusion-Aided Weights for Local Stereo Matching

Recently, local stereo matching has experienced largeprogress by the introduction of adaptive support-weights. Inthis paper, we aim at eliminating negative effects of occlusionsby proposing an occlusion-based method to improvetraditional support weights. Weights of occluded points aregreatly reduced while computing matching costs, initial disparitiesand final disparities. Experimental results on the Middlebury images demonstratethat our method is very effective in improving disparitiesof points around occluded areas and depth discontinuities.According to the Middlebury benchmark, theproposed algorithm is now the top performer among localstereo methods. Moreover, this approach can be easily integratedinto nearly all existing support weights strategies.