Dual-stream stereo network for depth estimation

Depth-estimation is an important task for autonomous driving, 3D object detection and recognition, scene understanding, and other fields. To improve the quality of depth estimation in high-frequency edge detail area, this paper presents DSS-Net, a dual-stream stereo network, combining a bottom-up steam based on scene understanding and a top-down stream based on parallax local optimization. Firstly, in the bottom-up stream, deep features containing high-level semantic information are extracted by a deep network, and then, a coarse estimate of the disparity is computed according to depth intervals classified based on deep features. In the up-bottom stream, the model uses the high-resolution shallow features with rich details and the initial coarse disparity to construct the local dense matching cost in the parallax neighborhood of each pixel of the initial disparity map, and uses stacked multiple hourglass networks to refine the parallax diagram in several stages. We achieve results on the Scene Flow, KITTI 2012 and 2015 datasets, showing that our method has high precision.