  Recently, leveraging on the development of end-to-end convolutional neural
networks (CNNs), deep stereo matching networks have achieved remarkable
performance far exceeding traditional approaches. However, state-of-the-art
stereo frameworks still have difficulties at finding correct correspondences in
texture-less regions, detailed structures, small objects and near boundaries,
which could be alleviated by geometric clues such as edge contours and
corresponding constraints. To improve the quality of disparity estimates in
these challenging areas, we propose an effective multi-task learning network,
EdgeStereo, composed of a disparity estimation branch and an edge detection
branch, which enables end-to-end predictions of both disparity map and edge
map. To effectively incorporate edge cues, we propose the edge-aware smoothness
loss and edge feature embedding for inter-task interactions. It is demonstrated
that based on our unified model, edge detection task and stereo matching task
can promote each other. In addition, we design a compact module called residual
pyramid to replace the commonly-used multi-stage cascaded structures or 3-D
convolution based regularization modules in current stereo matching networks.
By the time of the paper submission, EdgeStereo achieves state-of-art
performance on the FlyingThings3D dataset, KITTI 2012 and KITTI 2015 stereo
benchmarks, outperforming other published stereo matching methods by a
noteworthy margin. EdgeStereo also achieves comparable generalization
performance for disparity estimation because of the incorporation of edge cues.
