@inproceedings{78194c5903aa418883e384b90161b4c6,
title = "Computing the stereo matching cost with a convolutional neural network",
abstract = "We present a method for extracting depth information from a rectified image pair. We train a convolutional neural network to predict how well two image patches match and use it to compute the stereo matching cost. The cost is refined by cross-based cost aggregation and semiglobal matching, followed by a left-right consistency check to eliminate errors in the occluded regions. Our stereo method achieves an error rate of 2.61% on the KITTI stereo dataset and is currently (August 2014) the top performing method on this dataset.",
author = "Jure {\v Z}bontar and {Le Cun}, Yann",
year = "2015",
month = oct,
day = "14",
doi = "10.1109/CVPR.2015.7298767",
language = "English (US)",
series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
publisher = "IEEE Computer Society",
pages = "1592--1599",
booktitle = "IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015",
note = "IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 ; Conference date: 07-06-2015 Through 12-06-2015",
}