  Modern neural network-based algorithms are able to produce highly accurate
depth estimates from stereo image pairs, nearly matching the reliability of
measurements from more expensive depth sensors. However, this accuracy comes
with a higher computational cost since these methods use network architectures
designed to compute and process matching scores across all candidate matches at
all locations, with floating point computations repeated across a match volume
with dimensions corresponding to both space and disparity. This leads to longer
running times to process each image pair, making them impractical for real-time
use in robots and autonomous vehicles. We propose a new stereo algorithm that
employs a significantly more efficient network architecture. Our method builds
an initial match cost volume using traditional matching costs that are fast to
compute, and trains a network to estimate disparity from this volume.
Crucially, our network only employs per-pixel and two-dimensional convolution
operations: to summarize the match information at each location as a
low-dimensional feature vector, and to spatially process these `cost-signature'
features to produce a dense disparity map. Experimental results on the KITTI
benchmark show that our method delivers competitive accuracy at significantly
higher speeds---running at 48 frames per second on a modern GPU.
