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.

本研究基于卷积神经网络为计算视差匹配的成本聚合，并针对 KITTI2012、KITTI2015 和 Middlebury 立体数据集对该方法进行了验证，结果表明其在所有三个数据集上优于其他方法。