Cross-Trees for Stereo Matching with Priors

We propose a cross-trees structure to perform the non-local cost aggregation for dense stereo matching. The cross-trees structure consists of a horizontal-tree and a vertical-tree. Compared to other spanning trees, the significant superiority of the cross-trees is that the trees´ constructions are efficient and independent on any local or global property. Moreover, the trees are exactly unique. By traversing the two crossed trees successively, a fast non-local cost aggregation algorithm is performed to filter the matching cost volume and then the disparity maps are established with the Winner-Take-All (WTA) strategy. Additionally, two different priors: edge prior and super pixel prior, are proposed to tackle the false smoothing at the depth boundaries. Hence, our method contains two different algorithms in terms of the cross-trees prior in this paper. Performance evaluation on the 27 Middlebury data sets shows that both our algorithms outperform the other two tree-based methods, namely minimum spanning tree (MST) and segment-tree (ST). By performing the non-local cost aggregation on different trees, MST, ST and our method all have competitive rankings on the Middlebury website compared to the local cost aggregation methods.