title: Cross-trees, Edge and Superpixel Priors-based Cost aggregation for Stereo matching.
abstract: In this paper, we propose a novelcross-treesstructure to perform the nonlocal cost aggregation strategy, and thecross-treesstructure consists of ahorizontal-treeand avertical-tree. Compared to other spanning trees, the significant superiorities of thecross-treesare that the trees' constructions are efficient and the trees are exactly unique since the constructions are independent on any local or global property of the image itself. Additionally, two different priors:edge priorandsuperpixel prior, are proposed to tackle the false cost aggregations which cross the depth boundaries. Hence, our method contains two different algorithms in terms ofcross-trees+prior. By traversing the two crossed trees successively, a fast non-local cost aggregation algorithm is performed twice to compute the aggregated cost volume. Performance evaluation on the 27 Middlebury data sets shows that both our algorithms outperform the other two tree-based non-local methods, namelyminimum spanning tree(MST) andsegment-tree(ST).
authors.affiliation: Image Research Center, Beihang University, Beijing, China.
authors.forename: Feiyang
authors.initials: F
authors.lastname: Cheng
authors.affiliation: Image Research Center, Beihang University, Beijing, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Neurosurgery, University of Pittsburgh, Pittsburgh, USA.
authors.forename: Mingui
authors.initials: M
authors.lastname: Sun
authors.affiliation: Image Research Center, Beihang University, Beijing, China.
authors.forename: Ding
authors.initials: D
authors.lastname: Yuan
keywords: cost aggregation; image filtering; spanning trees; stereo matching