Cross-trees, edge and superpixel priors-based cost aggregation for stereo matching
We propose a novel cross-trees structure to perform the non-local cost aggregation. The trees? constructions are unique and independent on the image itself. We propose and incorporate two priors into the non-local framework. New edge weights function is designed according to the trees and the priors. Optimal support regions can be chose by cutting the cost aggregation flow on paths.
cost aggregation stereo matching image filtering spanning trees