  Recent work has shown that depth estimation from a stereo pair of images can
be formulated as a supervised learning task to be resolved with convolutional
neural networks (CNNs). However, current architectures rely on patch-based
Siamese networks, lacking the means to exploit context information for finding
correspondence in illposed regions. To tackle this problem, we propose PSMNet,
a pyramid stereo matching network consisting of two main modules: spatial
pyramid pooling and 3D CNN. The spatial pyramid pooling module takes advantage
of the capacity of global context information by aggregating context in
different scales and locations to form a cost volume. The 3D CNN learns to
regularize cost volume using stacked multiple hourglass networks in conjunction
with intermediate supervision. The proposed approach was evaluated on several
benchmark datasets. Our method ranked first in the KITTI 2012 and 2015
leaderboards before March 18, 2018. The codes of PSMNet are available at:
https://github.com/JiaRenChang/PSMNet.
