  Instance segmentation on point clouds is a fundamental task in 3D scene
perception. In this work, we propose a concise clustering-based framework named
HAIS, which makes full use of spatial relation of points and point sets.
Considering clustering-based methods may result in over-segmentation or
under-segmentation, we introduce the hierarchical aggregation to progressively
generate instance proposals, i.e., point aggregation for preliminarily
clustering points to sets and set aggregation for generating complete instances
from sets. Once the complete 3D instances are obtained, a sub-network of
intra-instance prediction is adopted for noisy points filtering and mask
quality scoring. HAIS is fast (only 410ms per frame) and does not require
non-maximum suppression. It ranks 1st on the ScanNet v2 benchmark, achieving
the highest 69.9% AP50 and surpassing previous state-of-the-art (SOTA) methods
by a large margin. Besides, the SOTA results on the S3DIS dataset validate the
good generalization ability. Code will be available at
https://github.com/hustvl/HAIS.
