  State-of-the-art models on contemporary 3D segmentation benchmarks like
ScanNet consume and label dataset-provided 3D point clouds, obtained through
post processing of sensed multiview RGB-D images. They are typically trained
in-domain, forego large-scale 2D pre-training and outperform alternatives that
featurize the posed RGB-D multiview images instead. The gap in performance
between methods that consume posed images versus post-processed 3D point clouds
has fueled the belief that 2D and 3D perception require distinct model
architectures. In this paper, we challenge this view and propose ODIN
(Omni-Dimensional INstance segmentation), a model that can segment and label
both 2D RGB images and 3D point clouds, using a transformer architecture that
alternates between 2D within-view and 3D cross-view information fusion. Our
model differentiates 2D and 3D feature operations through the positional
encodings of the tokens involved, which capture pixel coordinates for 2D patch
tokens and 3D coordinates for 3D feature tokens. ODIN achieves state-of-the-art
performance on ScanNet200, Matterport3D and AI2THOR 3D instance segmentation
benchmarks, and competitive performance on ScanNet, S3DIS and COCO. It
outperforms all previous works by a wide margin when the sensed 3D point cloud
is used in place of the point cloud sampled from 3D mesh. When used as the 3D
perception engine in an instructable embodied agent architecture, it sets a new
state-of-the-art on the TEACh action-from-dialogue benchmark. Our code and
checkpoints can be found at the project website (https://odin-seg.github.io).
