  To improve the performance in ill-posed regions, this paper proposes an
atrous granular multi-scale network based on depth edge
subnetwork(Dedge-AGMNet). According to a general fact, the depth edge is the
binary semantic edge of instance-sensitive. This paper innovatively generates
the depth edge ground-truth by mining the semantic and instance dataset
simultaneously. To incorporate the depth edge cues efficiently, our network
employs the hard parameter sharing mechanism for the stereo matching branch and
depth edge branch. The network modifies SPP to Dedge-SPP, which fuses the depth
edge features to the disparity estimation network. The granular convolution is
extracted and extends to 3D architecture. Then we design the AGM module to
build a more suitable structure. This module could capture the multi-scale
receptive field with fewer parameters. Integrating the ranks of different
stereo datasets, our network outperforms other stereo matching networks and
advances state-of-the-art performances on the Sceneflow, KITTI 2012 and KITTI
2015 benchmark datasets.
