Dedge-AGMNet:an effective stereo matching network optimized by depth edge auxiliary task
This improves stereo matching accuracy for applications like autonomous driving and robotics, though it appears incremental as it builds on existing network architectures with novel modifications.
The paper tackles the problem of stereo matching in ill-posed regions by proposing Dedge-AGMNet, which integrates depth edge cues through a novel auxiliary task and multi-scale architecture. It achieves state-of-the-art performance on Sceneflow, KITTI 2012, and KITTI 2015 benchmark datasets.
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.