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.
Zbontar et al. REF applied the convolution neural network approach to predict the image patches matching, and used it to compute the stereo matching cost, which was further refined by cross-based cost aggregation and semi-global matching.
Computing the Stereo Matching Cost with a Convolutional Neural Network
ArXiv
ArXiv
Computer Science