  A 3D point cloud describes the real scene precisely and intuitively.To date
how to segment diversified elements in such an informative 3D scene is rarely
discussed. In this paper, we first introduce a simple and flexible framework to
segment instances and semantics in point clouds simultaneously. Then, we
propose two approaches which make the two tasks take advantage of each other,
leading to a win-win situation. Specifically, we make instance segmentation
benefit from semantic segmentation through learning semantic-aware point-level
instance embedding. Meanwhile, semantic features of the points belonging to the
same instance are fused together to make more accurate per-point semantic
predictions. Our method largely outperforms the state-of-the-art method in 3D
instance segmentation along with a significant improvement in 3D semantic
segmentation. Code has been made available at:
https://github.com/WXinlong/ASIS.
