In this paper, we formulate the stereo matching problem as a Markov network consisting of three coupled Markov random fields (MRF's). These three MRF's model a smooth field for depth/disparity, a line process for depth discontinuity and a binary process for occlusion, respectively. After eliminating the line process and the binary process by introducing two robust functions, we obtain the maximum a posteriori (MAP) estimation in the Markov network by applying a Bayesian belief propagation (BP) algorithm. Furthermore, we extend our basic stereo model to incorporate other visual cues (e.g., image segmentation) that are not modeled in the three MRF's, and again obtain the MAP solution. Experimental results demonstrate that our method outperforms the state-of-art stereo algorithms for most test cases.
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