Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 28, Issue 8, Pages 3885-3897Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2019.2903318
Keywords
Stereo vision; disparity refinement; Markov random fields; RANSAC; Bayesian inference
Funding
- National Natural Science Foundation of China [61773365]
- Major Project of the Guangdong Province Science and Technology Department [2014B090919002]
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In this paper, we propose a disparity refinement method that directly refines the winner-take-all (WTA) disparity map by exploring its statistical significance. According to the primary steps of the segment-based stereo matching, the reference image is over-segmented into superpixels and a disparity plane is fitted f o r each superpixel by an improved random sample consensus (RANSAC). We design a two-layer optimization to refine the disparity plane. In the global optimization, mean disparities of superpixels are estimated by Markov random field (MRF) inference, and then, a 3D neighborhood system is derived from the mean disparities for occlusion handling. In the local optimization, a probability model exploiting Bayesian inference and Bayesian prediction is adopted and achieves second-order smoothness implicitly among 3D neighbors. The two-layer optimization is a pure disparity refinement method because no correlation information between stereo image pairs is demanded during the refinement. Experimental results on the Middlebury and KITTI datasets demonstrate that the proposed method can perform accurate stereo matching with a faster speed and handle the occlusion effectively. It can be indicated that the matching cost computation + disparity refinement framework is a possible solution to produce accurate disparity map at low computational cost.
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