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This paper tackles the problem of novel view synthesis from a single image. In particular, we target real-world scenes with rich geometric structure, a challenging task due to the large appearance variations of such scenes and the lack of simple 3D models to represent them. Modern, learning-based approaches mostly focus on appearance to synthesize novel views and thus tend to generate predictions that are inconsistent with the underlying scene structure. By contrast, in this paper, we proposedoi:10.1109/cvpr.2018.00485 dblp:conf/cvpr/LiuHS18 fatcat:x4ss6im5zbfjnjkx2csx3oxite