Semi-global Stereo Matching with Surface Orientation Priors
By Daniel Scharstein, Tatsunori Taniai, Sudipta N. Sinha. 2017
32 citations recorded by the source corpus.
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.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
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Citations in the corpus
- IRS: A Large Synthetic Indoor Robotics Stereo Dataset for Disparity and Surface Normal Estimation (2019, methodology): “Compared to monocular disparity estimation and normal estimation which rely on the prior information of the scene and lack geometric constraints, stereo vision can combine the prior information and geometry information together to give better estimated results [7], [8], [9].”