@inproceedings{scharstein2017semi-global,
author = {Scharstein, Daniel and Taniai, Tatsunori and Sinha, Sudipta},
title = {Semi-Global Stereo Matching with Surface Orientation Priors},
booktitle = {International Conference on 3D Vision (3DV), 2017},
year = {2017},
month = {September},
abstract = {Semi-Global Matching (SGM) is a widely-used efficient
stereo matching technique. It works well for textured
scenes, but fails on untextured slanted surfaces due to its
fronto-parallel smoothness assumption. To remedy this
problem, we propose a simple extension, termed SGM-P, to
utilize precomputed surface orientation priors. Such priors
favor different surface slants in different 2D image regions
or 3D scene regions and can be derived in various
ways. In this paper we evaluate plane orientation priors derived
from stereo matching at a coarser resolution and show
that such priors can yield significant performance gains for
difficult weakly-textured scenes. We also explore surface
normal priors derived from Manhattan-world assumptions,
and we analyze the potential performance gains using oracle
priors derived from ground-truth data. SGM-P only
adds a minor computational overhead to SGM and is an
attractive alternative to more complex methods employing
higher-order smoothness terms.},
url = {http://approjects.co.za/?big=en-us/research/publication/semi-global-stereo-matching-surface-orientation-priors/},
edition = {International Conference on 3D Vision (3DV), 2017},
}