Abstract Large-scale computing and high matching error rate are two disadvantages of the existing algorithms based on belief propagation. An efficient global optimal algorithm for dense disparity mapping is presented. Firstly, according to the feature of match cost and the disparity discontinuities accompanying the intensity changes, the adapted data constraint and the smoothness constraint are defined, and the messages are passed after the smoothness constraint is adjusted in every level. Then, the redundancy in message passing iteration process is discussed, and the message convergence is checked to decrease the running time. Finally, match symmetry is used to detect occlusions according to the analysis of matching errors in belief propagation algorithm. After the data term is reconstructed, a greedy method is used to iteratively refine the disparity result to propagate disparity information from the stable pixels to the unstable ones. The experimental results show the proposed algorithm computes a disparity map accurately with relative less time.
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2010, Vol. 23 
