In this paper, we propose a disparity refinement method that directly refines the winner-take-all (WTA) disparity map by exploring its statistical significance. According to the primary steps of the segment-based stereo matching, the reference image is over-segmented into super pixels and a disparity plane is fitted for each super pixel by an improved random sample consensus (RANSAC). We design a two-layer optimization to refine the disparity plane. In the global optimization, mean disparities of super pixels are estimated by Markov Random Fields (MRF) inference and then a 3D neighborhood system is derived from the mean disparities for occlusion handling. Inthe local optimization, a probability model exploiting Bayesian inference and Bayesian prediction is adopted and achieves second order smoothness implicitly among 3D neighbors. The two-layer optimization is a pure disparity refinement method because no correlation information between stereo image pairs is demanded during the refinement. Experimental results on the Middle bury and the KITTI dataset demonstrate that the proposed method can perform accurate stereo matching with a faster speed and handle the occlusion effectively. It can be indicated that the matching cost computation + disparity refinement framework is a possible solution to produce accurate disparity map at low computational cost.
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