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Monocular depth estimation is a challenging task in scene understanding, with the goal to acquire the geometric properties of 3D space from 2D images. Due to the lack of RGB-depth image pairs, unsupervised learning methods aim at deriving depth information with alternative supervision such as stereo pairs. However, most existing works fail to model the geometric structure of objects, which generally results from considering pixel-level objective functions during training. In this paper, wedoi:10.1109/cvpr.2019.00273 dblp:conf/cvpr/ChenLLW19 fatcat:yxy47fjnnbchljkxlmge7fte3u