Papers › Relation3D : Enhancing Relation Modeling for Point Cloud Instance Segmentation
Relation3D : Enhancing Relation Modeling for Point Cloud Instance Segmentation
Jiahao Lu, Jiacheng Deng
3D instance segmentation aims to predict a set of object instances in a scene, representing them as binary foreground masks with corresponding semantic labels. Currently, transformer-based methods are gaining increasing attention due to their elegant pipelines and superior predictions. However, these methods primarily focus on modeling the external relationships between scene features and query features through mask attention. They lack effective modeling of the internal relationships among scene features as well as between query features.In light of these disadvantages, we propose Relation3D: Enhancing Relation Modeling for Point Cloud Instance Segmentation. Specifically, we introduce an adaptive superpoint aggregation module and a contrastive learning-guided superpoint refinement module to better represent superpoint features (scene features) and leverage contrastive learning to guide the updates of these features.Furthermore, our relation-aware self-attention mechanism enhances the capabilities of modeling relationships between queries by incorporating positional and geometric relationships into the self-attention mechanism.Extensive experiments on the ScanNetV2, ScanNet++, ScanNet200 and S3DIS datasets demonstrate the superior performance of Relation3D.
Code
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Instance Segmentation | ScanNet(v2) | Relation3D | mAP | 62.2 | #1 of 32 | Archive leaderboard | report |
| 3D Instance Segmentation | ScanNet(v2) | Relation3D | mAP @ 50 | 81.6 | #1 of 32 | Archive leaderboard | report |
| 3D Instance Segmentation | ScanNet(v2) | Relation3D | mAP@25 | 90.1 | #1 of 32 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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