A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Stereo Matching Using Belief Propagation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Markov Random Field Modeling in Image Analysis
Markov Random Field Modeling in Image Analysis
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
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Stereo correspondence and virtual view synthesis play a crucial role in three-dimensional video systems. In this paper, the traditional stereo correspondence problem is modeled on Markov Random Fields (MRF) where the matching consistency is optimized by minimizing the global energy function. Both of the left-to-right disparity map and right-to-left disparity map are estimated simultaneously with the max-product belief propagation algorithm. The experimental results show that high quality intermediate views with significantly reduced hole regions can be obtained based on the bidirectional disparity maps.