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Stereo matching using belief propagation

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3 Author(s)
Jian Sun ; Inst. of Artificial Intelligence & Robotics, Xi'an Jiaotong Univ., China ; Nan-Ning Zheng ; Heung-Yeung Shum

In this paper, we formulate the stereo matching problem as a Markov network and solve it using Bayesian belief propagation. The stereo Markov network consists of three coupled Markov random fields that model the following: a smooth field for depth/disparity, a line process for depth discontinuity, and a binary process for occlusion. After eliminating the line process and the binary process by introducing two robust functions, we apply the belief propagation algorithm to obtain the maximum a posteriori (MAP) estimation in the Markov network. Other low-level visual cues (e.g., image segmentation) can also be easily incorporated in our stereo model to obtain better stereo results. Experiments demonstrate that our methods are comparable to the state-of-the-art stereo algorithms for many test cases.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:25 ,  Issue: 7 )