Papers › Pamba: Enhancing Global Interaction in Point Clouds via State Space Model
Pamba: Enhancing Global Interaction in Point Clouds via State Space Model
Zhuoyuan Li, Yubo Ai, Jiahao Lu, Chuxin Wang, Jiacheng Deng, Hanzhi Chang, Yanzhe Liang, Wenfei Yang, Shifeng Zhang, Tianzhu Zhang
Transformers have demonstrated impressive results for 3D point cloud semantic segmentation. However, the quadratic complexity of transformer makes computation costs high, limiting the number of points that can be processed simultaneously and impeding the modeling of long-range dependencies between objects in a single scene. Drawing inspiration from the great potential of recent state space models (SSM) for long sequence modeling, we introduce Mamba, an SSM-based architecture, to the point cloud domain and propose Pamba, a novel architecture with strong global modeling capability under linear complexity. Specifically, to make the disorderness of point clouds fit in with the causal nature of Mamba, we propose a multi-path serialization strategy applicable to point clouds. Besides, we propose the ConvMamba block to compensate for the shortcomings of Mamba in modeling local geometries and in unidirectional modeling. Pamba obtains state-of-the-art results on several 3D point cloud segmentation tasks, including ScanNet v2, ScanNet200, S3DIS and nuScenes, while its effectiveness is validated by extensive experiments.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Semantic Segmentation | ScanNet200 | Pamba | test mIoU | 37.1 | #6 of 16 | Archive leaderboard | report |
| 3D Semantic Segmentation | ScanNet200 | Pamba | val mIoU | 36.3 | #6 of 16 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS Area5 | Pamba | mIoU | 73.5 | #12 of 61 | Archive leaderboard | report |
| Semantic Segmentation | ScanNet | Pamba | val mIoU | 77.6 | #7 of 45 | 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.
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