Recently, leveraging on the development of end-to-end convolutional neural networks, deep stereo matching networks achieve remarkable performance far exceeding traditional approaches. However, state-of-the-art stereo methods still have difficulties 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 sub-network and an edge detection sub-network, which enables end-to-end predictions of both disparity map and edge map. To effectively incorporates 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-the-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 has a better generalization capability for disparity estimation because of the incorporation of edge cues.

深度立体匹配网络结合边缘检测网络，构成一种多任务学习网络，其中边缘线索提供了出色的线索，以实现细节区域的视差估计精度的提高，并获得 state-of-the-art 的表现。

EdgeStereo：用于立体匹配和边缘检测的有效多任务学习网络