Pri3D: Can 3D Priors Help 2D Representation Learning?

• URL: http://arxiv.org/abs/2104.11225v1
• Date: Thu, 22 Apr 2021 17:59:30 GMT
• Title: Pri3D: Can 3D Priors Help 2D Representation Learning?
• Authors: Ji Hou, Saining Xie, Benjamin Graham, Angela Dai, Matthias Nie{\ss}ner
• Abstract summary: We introduce an approach to learn view-invariant,geometry-aware representations for network pre-training. We employ contrastive learning under both multi-view im-age constraints and image-geometry constraints to encode3D priors into learned 2D representations.
• Score: 37.35721274841419
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in 3D perception have shown impressive progress in understanding geometric structures of 3Dshapes and even scenes. Inspired by these advances in geometric understanding, we aim to imbue image-based perception with representations learned under geometric constraints. We introduce an approach to learn view-invariant,geometry-aware representations for network pre-training, based on multi-view RGB-D data, that can then be effectively transferred to downstream 2D tasks. We propose to employ contrastive learning under both multi-view im-age constraints and image-geometry constraints to encode3D priors into learned 2D representations. This results not only in improvement over 2D-only representation learning on the image-based tasks of semantic segmentation, instance segmentation, and object detection on real-world in-door datasets, but moreover, provides significant improvement in the low data regime. We show a significant improvement of 6.0% on semantic segmentation on full data as well as 11.9% on 20% data against baselines on ScanNet.

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