Papers › Point Cloud Instance Segmentation using Probabilistic Embeddings
Point Cloud Instance Segmentation using Probabilistic Embeddings
Biao Zhang, Peter Wonka
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Instance Segmentation | PartNet | Probabilistic Embeddings | mAP50 | 57.5 | #2 of 3 | Archive leaderboard | report |
| Instance Segmentation | PartNet | PE | mAP50 | 57.5 | #1 of 1 | Archive leaderboard | report |
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