  We present an improved three-step pipeline for the stereo matching problem
and introduce multiple novelties at each stage. We propose a new highway
network architecture for computing the matching cost at each possible
disparity, based on multilevel weighted residual shortcuts, trained with a
hybrid loss that supports multilevel comparison of image patches. A novel
post-processing step is then introduced, which employs a second deep
convolutional neural network for pooling global information from multiple
disparities. This network outputs both the image disparity map, which replaces
the conventional "winner takes all" strategy, and a confidence in the
prediction. The confidence score is achieved by training the network with a new
technique that we call the reflective loss. Lastly, the learned confidence is
employed in order to better detect outliers in the refinement step. The
proposed pipeline achieves state of the art accuracy on the largest and most
competitive stereo benchmarks, and the learned confidence is shown to
outperform all existing alternatives.
