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Real-time Global Stereo Matching Using Hierarchical Belief Propagation

Real-time Global Stereo Matching Using Hierarchical Belief Propagation,Qingxiong Yang,Liang Wang,Ruigang Yang,Shengnan Wang,Miao Liao,David Nistér

Real-time Global Stereo Matching Using Hierarchical Belief Propagation   (Citations: 77)
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In this paper, we present a belief propagation based global algorithm that gen- erates high quality results while maintaining real-time performance. To our knowledge, it is the first BP based global method that runs at real-time speed. Our efficiency performance gains mainly from the parallelism of graphics hardware,which leads to a 45 times speedup compared to the CPU imple- mentation. To qualify the accurancy of our approach, the experimental re- sults are evaluated on the Middlebury data sets, showing that our approach is among the best (ranked first in the new evaluation system) for all real-time approaches. In addition, since the running time of general BP is linear to the number of iterations, adopting a large number of iterations is not feasible for practical applications. Hence a novel approach is proposed to adaptively up- date pixel cost. Unlike general BP methods, the running time of our proposed algorithm dramatically converges.
Conference: British Machine Vision Conference - BMVC , pp. 989-998, 2006
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