Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
This work addresses depth extraction from stereo images for applications like robotics and autonomous driving, presenting a novel method for matching cost computation.
The paper tackles stereo matching by learning a similarity measure for image patches using a convolutional neural network to compute matching costs, and it outperforms other methods on KITTI 2012, KITTI 2015, and Middlebury datasets.
We present a method for extracting depth information from a rectified image pair. Our approach focuses on the first stage of many stereo algorithms: the matching cost computation. We approach the problem by learning a similarity measure on small image patches using a convolutional neural network. Training is carried out in a supervised manner by constructing a binary classification data set with examples of similar and dissimilar pairs of patches. We examine two network architectures for this task: one tuned for speed, the other for accuracy. The output of the convolutional neural network is used to initialize the stereo matching cost. A series of post-processing steps follow: cross-based cost aggregation, semiglobal matching, a left-right consistency check, subpixel enhancement, a median filter, and a bilateral filter. We evaluate our method on the KITTI 2012, KITTI 2015, and Middlebury stereo data sets and show that it outperforms other approaches on all three data sets.