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We present a method for extracting depth information from a rectified imagepair. Our approach focuses on the first stage of many stereo algorithms: thematching cost computation. We approach the problem by learning a similaritymeasure on small image patches using a convolutional neural network. Trainingis carried out in a supervised manner by constructing a binary classificationdata set with examples of similar and dissimilar pairs of patches. We examinetwo network architectures for this task: one tuned for speed, the other foraccuracy. The output of the convolutional neural network is used to initializethe stereo matching cost. A series of post-processing steps follow: cross-basedcost aggregation, semiglobal matching, a left-right consistency check, subpixelenhancement, a median filter, and a bilateral filter. We evaluate our method onthe KITTI 2012, KITTI 2015, and Middlebury stereo data sets and show that itoutperforms other approaches on all three data sets.

Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches