Hierarchical Stereo with Thin Structures and Transparency

Dense stereo algorithms rely on matching over a range of disparities. To speed up the search and reduce match ambiguity, processing can be embedded in the hierarchical, or coarse-to-fine (CTF), framework using image pyramids. However, this technique is limited when resolving thin structures, as they are poorly represented at coarser scales. In this paper we exploit alternative pyramid and search space techniques. We propose matching with the Magnitude-extended Laplacian Pyramid (MeLP) - a generalization of the Laplacian pyramid that explicitly encodes the energy magnitude component of the band-passed images. In essence, MeLP effectively encodes fine scale details in low resolution images, which allows for accurate recovery of thin structures during CTF processing. Furthermore, transparencies can be resolved for common cases when spatial frequency structure is locally different for each layer. Algorithmic instantiations for local block matching and global Graph Cuts formulations are presented. Extensive experimental evaluation demonstrates the benefits of the proposed techniques.