Normal Assisted Stereo Depth Estimation

Uday Kusupati, Shuo Cheng, Rui Chen, Hao Su
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
Accurate stereo depth estimation plays a critical role in various 3D tasks in both indoor and outdoor environments. Recently, learning-based multi-view stereo methods have demonstrated competitive performance with limited number of views. However, in challenging scenarios, especially when building cross-view correspondences is hard, these methods still cannot produce satisfying results. In this paper, we study how to leverage a normal estimation model and the predicted normal maps to improve
more » ... depth quality. We couple the learning of a multi-view normal estimation module and a multi-view depth estimation module. In addition, we propose a novel consistency loss to train an independent consistency module that refines the depths from depth/normal pairs. We find that the joint learning can improve both the prediction of normal and depth, and the accuracy & smoothness can be further improved by enforcing the consistency. Experiments on MVS, SUN3D, RGBD and Scenes11 demonstrate the effectiveness of our method and state-of-the-art performance.
doi:10.1109/cvpr42600.2020.00226 dblp:conf/cvpr/KusupatiCCS20 fatcat:wf4eddidhrdytduzfx22ezd3ha