  In this paper, a new deep learning architecture for stereo disparity
estimation is proposed. The proposed atrous multiscale network (AMNet) adopts
an efficient feature extractor with depthwise-separable convolutions and an
extended cost volume that deploys novel stereo matching costs on the deep
features. A stacked atrous multiscale network is proposed to aggregate rich
multiscale contextual information from the cost volume which allows for
estimating the disparity with high accuracy at multiple scales. AMNet can be
further modified to be a foreground-background aware network, FBA-AMNet, which
is capable of discriminating between the foreground and the background objects
in the scene at multiple scales. An iterative multitask learning method is
proposed to train FBA-AMNet end-to-end. The proposed disparity estimation
networks, AMNet and FBA-AMNet, show accurate disparity estimates and advance
the state of the art on the challenging Middlebury, KITTI 2012, KITTI 2015, and
Sceneflow stereo disparity estimation benchmarks.
