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Automatic Dense Annotation for Monocular 3D Scene Understanding
Deep neural networks have revolutionized many areas of computer vision, but they require notoriously large amounts of labeled training data. For tasks such as semantic segmentation and monocular 3d scene layout estimation, collecting high-quality training data is extremely laborious because dense, pixellevel ground truth is required and must be annotated by hand. In this paper, we present two techniques for significantly reducing the manual annotation effort involved in collecting largedoi:10.1109/access.2020.2984745 fatcat:b6njvki67bbr7efdglfohyi3gq