Leveraging stereo matching with learning-based confidence measures

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We propose a new approach to associate supervised learning-based confidence prediction with the stereo matching problem. First of all, we analyze the characteristics of various confidence measures in the regression forest framework to select effective confidence measures using training data. We then train regression forests again to predict the correctness (confidence) of a match by using selected confidence measures. In addition, we present a confidence-based matching cost modulation scheme based on the predicted correctness for improving the robustness and accuracy of various stereo matching algorithms. We apply the proposed scheme to the semi-global matching algorithm to make it robust under unexpected difficulties that can occur in outdoor environments. We verify the proposed confidence measure selection and cost modulation methods through extensive experimentation with various aspects using KITTI and challenging outdoor datasets.
Publisher
IEEE Computer Society and the Computer Vision Foundation (CVF)
Issue Date
2015-06-07
Language
English
Citation

IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, pp.101 - 109

ISSN
1063-6919
DOI
10.1109/CVPR.2015.7298605
URI
http://hdl.handle.net/10203/244629
Appears in Collection
ME-Conference Papers(학술회의논문)
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