Point Cloud Instance Segmentation using Probabilistic Embeddings

Biao Zhang; Peter Wonka

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arXiv · 1912.00145

Point Cloud Instance Segmentation using Probabilistic Embeddings

Biao Zhang·Peter Wonka

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Abstract

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.

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BibTeX RIS

Biao Zhang, Peter Wonka. 2019-11-30. Point Cloud Instance Segmentation using Probabilistic Embeddings. https://arxiv.org/abs/1912.00145

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