Monocular Depth Estimation with Self-supervised Instance Adaptation

• URL: http://arxiv.org/abs/2004.05821v1
• Date: Mon, 13 Apr 2020 08:32:03 GMT
• Title: Monocular Depth Estimation with Self-supervised Instance Adaptation
• Authors: Robert McCraith, Lukas Neumann, Andrew Zisserman, Andrea Vedaldi
• Abstract summary: In robotics applications, multiple views of a scene may or may not be available, depend-ing on the actions of the robot. We propose a new approach that extends any off-the-shelf self-supervised monocular depth reconstruction system to usemore than one image at test time.
• Score: 138.0231868286184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in self-supervised learning havedemonstrated that it is possible to learn accurate monoculardepth reconstruction from raw video data, without using any 3Dground truth for supervision. However, in robotics applications,multiple views of a scene may or may not be available, depend-ing on the actions of the robot, switching between monocularand multi-view reconstruction. To address this mixed setting,we proposed a new approach that extends any off-the-shelfself-supervised monocular depth reconstruction system to usemore than one image at test time. Our method builds on astandard prior learned to perform monocular reconstruction,but uses self-supervision at test time to further improve thereconstruction accuracy when multiple images are available.When used to update the correct components of the model, thisapproach is highly-effective. On the standard KITTI bench-mark, our self-supervised method consistently outperformsall the previous methods with an average 25% reduction inabsolute error for the three common setups (monocular, stereoand monocular+stereo), and comes very close in accuracy whencompared to the fully-supervised state-of-the-art methods.

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