  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.
