Novel Belief Propagation Algorithm for Stereo Matching with a Robust Cost Computation

Abstract

Stereo matching is a challenging problem in the field of computer vision and has recently received extensive attention. However, the traditional methods are labor intensive and premised on specific conditions. In this paper, we propose a robust stereo matching cost algorithm that relies on refined features extracted by a stack auto-encoder. These features are robust for different types of image pairs, which significantly improve the generality of the proposed matching algorithm. In addition, we smoothed the belief volume with a guided filter to improve the performance of the belief propagation algorithm on edge regions. To deal with the time consumption issue of the standard belief propagation algorithm, we also proposed a constraint method to speed it up. This constraint does not degrade the matching results and can easily be generalized to other message passing algorithms. The experiments conducted on a Middlebury benchmark dataset demonstrate the effectiveness of the proposed algorithms.