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In this paper, we extend the standard belief propagation (BP) sequentialtechnique proposed in the tree-reweighted sequential method to the fullyconnected CRF models with the geodesic distance affinity. The proposed methodhas been applied to the stereo matching problem. Also a new approach to the BPmarginal solution is proposed that we call one-view occlusion detection (OVOD).In contrast to the standard winner takes all (WTA) estimation, the proposedOVOD solution allows to find occluded regions in the disparity map andsimultaneously improve the matching result. As a result we can perform only oneenergy minimization process and avoid the cost calculation for the second viewand the left-right check procedure. We show that the OVOD approach considerablyimproves results for cost augmentation and energy minimization techniques incomparison with the standard one-view affinity space implementation. We applyour method to the Middlebury data set and reach state-of-the-art especially formedian, average and mean squared error metrics.

One-view occlusion detection for stereo matching with a fully connected CRF model