  Our goal here is threefold: [1] To present a new dense-stereo matching
algorithm, tMGM, that by combining the hierarchical logic of tSGM with the
support structure of MGM achieves 6-8\% performance improvement over the
baseline SGM (these performance numbers are posted under tMGM-16 in the
Middlebury Benchmark V3 ); and [2] Through an exhaustive quantitative and
qualitative comparative study, to compare how the major variants of the SGM
approach to dense stereo matching, including the new tMGM, perform in the
presence of: (a) illumination variations and shadows, (b) untextured or weakly
textured regions, (c) repetitive patterns in the scene in the presence of large
stereo rectification errors. [3] To present a novel DEM-Sculpting approach for
estimating initial disparity search bounds for multi-date satellite stereo
pairs. Based on our study, we have found that tMGM generally performs best with
respect to all these data conditions. Both tSGM and MGM improve the density of
stereo disparity maps and combining the two in tMGM makes it possible to
accurately estimate the disparities at a significant number of pixels that
would otherwise be declared invalid by SGM. The datasets we have used in our
comparative evaluation include the Middlebury2014, KITTI2015, and ETH3D
datasets and the satellite images over the San Fernando area from the MVS
Challenge dataset.
