Point Cloud Instance Segmentation using Probabilistic Embeddings

Biao Zhang, Peter Wonka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Scopus citations


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.
Original languageEnglish (US)
Title of host publication2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ISBN (Print)978-1-6654-4510-8
StatePublished - 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-11-05


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