Cross-Scale Local Stereo Matching Based on Edge Weighting

Abstract

To solve the problem of mismatch of the edge region in the local stereo matching algorithm, a cross-scale local stereo matching algorithm based on edge weighting is proposed. In the cost computation stage, an edge similarity measurement method is proposed according to the number and structural information of edge points, and the points satisfying the constraint conditions are weighted by two strategies. In this way, the recognition of corresponding points in the target and reference maps are improved. Cross-scale model is introduced in the cost aggregation stage, and guided filtering is used for aggregation. Finally, the disparity map is obtained by disparity computation and refinement. Four sets of standard stereo image pairs and 27 sets of extended stereo image pairs are tested on the Middlebury benchmark. The average mismatch rate of non-occlusion regions is 7.88% without any refinement steps. Experimental results show that the proposed algorithm effectively improves the matching accuracy of the edge region.