Semantically-Guided Representation Learning for Self-Supervised Monocular Depth

ICLR 2020 Vitor GuiziliniRui HouJie LiRares AmbrusAdrien Gaidon

Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning representations that relate visual appearance to 3D properties by implicitly leveraging category-level patterns... (read more)

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