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Recent advances in 3D perception have shown impressive progress inunderstanding geometric structures of 3Dshapes and even scenes. Inspired bythese advances in geometric understanding, we aim to imbue image-basedperception with representations learned under geometric constraints. Weintroduce an approach to learn view-invariant,geometry-aware representationsfor network pre-training, based on multi-view RGB-D data, that can then beeffectively transferred to downstream 2D tasks. We propose to employcontrastive learning under both multi-view im-age constraints andimage-geometry constraints to encode3D priors into learned 2D representations.This results not only in improvement over 2D-only representation learning onthe image-based tasks of semantic segmentation, instance segmentation, andobject detection on real-world in-door datasets, but moreover, providessignificant improvement in the low data regime. We show a significantimprovement of 6.0% on semantic segmentation on full data as well as 11.9% on20% data against baselines on ScanNet.

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