Abstract: The high erroneous results of the stereo matching occur at the following three cases where there are depth discontinuity region， the slanted surface or the non-fronto-parallel surface. A stereo matching algorithm was proposed based on the improved Patchmatch and slice sampling particle belief propagation. An edge-preserving similarity function of the Patchmatch was defined. Then， a model of the depth estimation for the non-fronto-parallel surface was introduced. The nearest neighbor search was replaced with the particle belief propagation， and the target distribution was approximated with a finite set of particles. At the same time， the sampled particles from the belief distribution was typically done by using slice sampling Markov chain Monte Carlo method to solve the particle update problem. The experiments on the Middlebury indicate that the mismatching at the depth discontinuity region can be reduced， and the match accuracy for the slanted surface and the non-fronto-parallel surface can be improved.

Key words: stereo matching, Patchmatch, particle belief propagation, slice sampling, Markov chain Monte Carlo