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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 improvedoi:10.1109/cvpr42600.2020.00226 dblp:conf/cvpr/KusupatiCCS20 fatcat:wf4eddidhrdytduzfx22ezd3ha