提出CloudMamba，用分组状态空间提升点云分析的精度与效率。
CloudMamba: Grouped Selective State Spaces for Point Cloud Analysis
- 分轴序列化+特征融合，让无序点云适配Mamba因果结构
- 双向链式Mamba捕获高层几何信息，提升感知能力
- 分组S6模型减少过拟合，复杂度显著降低
由于长程建模能力和线性复杂度，Mamba在点云分析中备受关注。然而现有方法仍存在点云序列化不完善、高层次几何感知不足以及Mamba核心模块S6的过拟合问题。为此，本文提出基于状态空间模型的点云网络CloudMamba。首先，设计序列扩展与序列合并机制，分别沿各轴独立序列化点，并因果融合不同序列对应的高阶特征，使无序点集更稳定地适应Mamba的因果性质，且无需额外参数。其次，提出chainedMamba，在并行双向Mamba中串联前后向过程，实现扫描时对高层几何信息的捕捉。此外，通过在S6上引入参数共享，提出分组选择性状态空间模型（GS6），缓解了S6计算模式带来的过拟合问题。在多个点云任务上的实验表明，CloudMamba在显著降低复杂度的前提下，实现了最先进性能。
原文摘要 · Abstract (English)
Due to the long-range modeling ability and linear complexity property, Mamba has attracted considerable attention in point cloud analysis. Despite some interesting progress, related work still suffers from imperfect point cloud serialization, insufficient high-level geometric perception, and overfitting of the selective state space model (S6) at the core of Mamba. To this end, we resort to an SSM-based point cloud network termed CloudMamba to address the above challenges. Specifically, we propose sequence expanding and sequence merging, where the former serializes points along each axis separately and the latter serves to fuse the corresponding higher-order features causally inferred from different sequences, enabling unordered point sets to adapt more stably to the causal nature of Mamba without parameters. Meanwhile, we design chainedMamba that chains the forward and backward processes in the parallel bidirectional Mamba, capturing high-level geometric information during scanning. In addition, we propose a grouped selective state space model (GS6) via parameter sharing on S6, alleviating the overfitting problem caused by the computational mode in S6. Experiments on various point cloud tasks validate CloudMamba's ability to achieve state-of-the-art results with significantly less complexity.
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