  In this paper we propose a new framework for point cloud instance
segmentation. Our framework has two steps: an embedding step and a clustering
step. In the embedding step, our main contribution is to propose a
probabilistic embedding space for point cloud embedding. Specifically, each
point is represented as a tri-variate normal distribution. In the clustering
step, we propose a novel loss function, which benefits both the semantic
segmentation and the clustering. Our experimental results show important
improvements to the SOTA, i.e., 3.1% increased average per-category mAP on the
PartNet dataset.
