  Semantic, instance, and panoptic segmentation of 3D point clouds have been
addressed using task-specific models of distinct design. Thereby, the
similarity of all segmentation tasks and the implicit relationship between them
have not been utilized effectively. This paper presents a unified, simple, and
effective model addressing all these tasks jointly. The model, named
OneFormer3D, performs instance and semantic segmentation consistently, using a
group of learnable kernels, where each kernel is responsible for generating a
mask for either an instance or a semantic category. These kernels are trained
with a transformer-based decoder with unified instance and semantic queries
passed as an input. Such a design enables training a model end-to-end in a
single run, so that it achieves top performance on all three segmentation tasks
simultaneously. Specifically, our OneFormer3D ranks 1st and sets a new
state-of-the-art (+2.1 mAP50) in the ScanNet test leaderboard. We also
demonstrate the state-of-the-art results in semantic, instance, and panoptic
segmentation of ScanNet (+21 PQ), ScanNet200 (+3.8 mAP50), and S3DIS (+0.8
mIoU) datasets.
