Iterative color-depth MST cost aggregation for stereo matching

The minimum spanning tree (MST) based non-local cost aggregation algorithm performs well in accuracy and time efficiency. However, it can still be improved in two aspects. First, we propose a logarithmic transformation on matching cost function to improve the matching efficiency in texture less regions. The textureless neighbors can provide effective contributions in cost aggregation by the proposed monotone increasing function. Hence the algorithm can distinguish different pixels in textureless regions. Second, MST algorithm only utilizes color information in weight function while aggregating, which leads 3D cues missing. We introduce depth weight computed from the original MST algorithm into an edge weight function. With the proposed color-depth weight, we further iteratively rebuild the tree and obtain enhanced disparity map. Performance evaluations on 19 Middlebury stereo pairs and Microsoft stereo videos show that the proposed algorithm outperforms than other five state-of-the-art cost aggregation algorithms.