Efficient High-Resolution Stereo Matching Using Local Plane Sweeps

We present a stereo algorithm designed for speed and efficiency that uses local slanted plane sweeps to propose disparity hypotheses for a semi-global matching algorithm. Our local plane hypotheses are derived from initial sparse feature correspondences followed by an iterative clustering step. Loca...

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Bibliographic Details
Published in2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 1582 - 1589
Main Authors Sinha, Sudipta Narayan, Scharstein, Daniel, Szeliski, Richard
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.06.2014
Subjects
Accuracy
Algorithms
Benchmark testing
Computer vision
Conferences
Equations
Hypotheses
Image resolution
Matching
Optimization
Parallax
Pattern recognition
plane sweep
Planes
Proposals
semi-global matching
stereo matching
Stereo vision
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Summary:We present a stereo algorithm designed for speed and efficiency that uses local slanted plane sweeps to propose disparity hypotheses for a semi-global matching algorithm. Our local plane hypotheses are derived from initial sparse feature correspondences followed by an iterative clustering step. Local plane sweeps are then performed around each slanted plane to produce out-of-plane parallax and matching-cost estimates. A final global optimization stage, implemented using semi-global matching, assigns each pixel to one of the local plane hypotheses. By only exploring a small fraction of the whole disparity space volume, our technique achieves significant speedups over previous algorithms and achieves state-of-the-art accuracy on high-resolution stereo pairs of up to 19 megapixels.
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ISSN:1063-6919
1063-6919
2575-7075
DOI:10.1109/CVPR.2014.205