Recent convolutional neural networks, especially end-to-end disparity
estimation models, achieve remarkable performance on stereo matching task.
However, existed methods, even with the complicated cascade structure, may fail
in the regions of non-textures, boundaries and tiny details. Focus on these
problems, we propose a multi-task network EdgeStereo that is composed of a
backbone disparity network and an edge sub-network. Given a binocular image
pair, our model enables end-to-end prediction of both disparity map and edge
map. Basically, we design a context pyramid to encode multi-scale context
information in disparity branch, followed by a compact residual pyramid for
cascaded refinement. To further preserve subtle details, our EdgeStereo model
integrates edge cues by feature embedding and edge-aware smoothness loss
regularization. Comparative results demonstrates that stereo matching and edge
detection can help each other in the unified model. Furthermore, our method
achieves state-of-art performance on both KITTI Stereo and Scene Flow
benchmarks, which proves the effectiveness of our design.

本文提出了一种多任务网络 EdgeStereo，由一个背景视差网络和一个边缘子网络组成，通过设计上下文金字塔来编码多尺度上下文信息，并通过特征嵌入和基于边缘感知的光滑度损失正则化来融合边缘线索。经过实验证明，立体匹配和边缘检测可以相互帮助，并且该方法在 KITTI 立体和 Scene Flow 基准测试中均取得了最佳性能。