Bayesian Stereo Matching Method Based on Edge Constraints.

Abstract

A new global stereo matching method is presented that focuses on the handling of disparity, discontinuity and occlusion. The Bayesian approach is utilized for dense stereo matching problem formulated as a maximum a posteriori Markov Random Field (MAP-MRF) problem. In order to improve stereo matching performance, edges are incorporated into the Bayesian model as a soft constraint. Accelerated belief propagation is applied to obtain the maximum a posteriori estimates in the Markov random field. The proposed algorithm is evaluated using the Middlebury stereo benchmark. Our experimental results comparing with some state-of-the-art stereo matching methods demonstrate that the proposed method provides superior disparity maps with a subpixel precision.