Leveraging Stereo Matching with Learning-Based Confidence Measures
Abstract
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.
Cite
Text
Park and Yoon. "Leveraging Stereo Matching with Learning-Based Confidence Measures." Conference on Computer Vision and Pattern Recognition, 2015. doi:10.1109/CVPR.2015.7298605Markdown
[Park and Yoon. "Leveraging Stereo Matching with Learning-Based Confidence Measures." Conference on Computer Vision and Pattern Recognition, 2015.](https://mlanthology.org/cvpr/2015/park2015cvpr-leveraging/) doi:10.1109/CVPR.2015.7298605BibTeX
@inproceedings{park2015cvpr-leveraging,
title = {{Leveraging Stereo Matching with Learning-Based Confidence Measures}},
author = {Park, Min-Gyu and Yoon, Kuk-Jin},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2015},
doi = {10.1109/CVPR.2015.7298605},
url = {https://mlanthology.org/cvpr/2015/park2015cvpr-leveraging/}
}