用多头状态空间模型提升点云全局建模能力，解决长距离依赖与局部特征学习难题。
HydraMamba: Multi-Head State Space Model for Global Point Cloud Learning
- 设计随机重排序列化策略，适配S6的因果特性提升点云处理效率
- 提出ConvBiS6层，同步捕捉局部几何与全局上下文依赖关系
- 引入多头S6结构，增强模型表达能力，在物体与场景级任务上达顶尖性能
注意力机制虽在点云学习中占主导地位，但其二次复杂度限制了点间交互，难以建模对象间的长程依赖。由于具备线性复杂度下的优秀长程建模能力，选择性状态空间模型（S6）作为Mamba核心，已被用于点云中全点集的长程依赖建模。尽管取得一定进展，现有方法仍存在点云序列化不完善及局部学习能力不足的问题。为此，本文提出基于状态空间模型的点云网络HydraMamba。我们设计了打乱序列化策略，使无序点集更适应S6的因果特性；同时提出ConvBiS6层，可协同捕捉局部几何与全局上下文依赖。此外，通过将多头设计扩展至S6，提出MHS6以进一步增强建模能力。HydraMamba在多个物体级与场景级任务上达到当前最优结果。代码已开源：https://github.com/Point-Cloud-Learning/HydraMamba。
原文摘要 · Abstract (English)
The attention mechanism has become a dominant operator in point cloud learning, but its quadratic complexity leads to limited inter-point interactions, hindering long-range dependency modeling between objects. Due to excellent long-range modeling capability with linear complexity, the selective state space model (S6), as the core of Mamba, has been exploited in point cloud learning for long-range dependency interactions over the entire point cloud. Despite some significant progress, related works still suffer from imperfect point cloud serialization and lack of locality learning. To this end, we explore a state space model-based point cloud network termed HydraMamba to address the above challenges. Specifically, we design a shuffle serialization strategy, making unordered point sets better adapted to the causal nature of S6. Meanwhile, to overcome the deficiency of existing techniques in locality learning, we propose a ConvBiS6 layer, which is capable of capturing local geometries and global context dependencies synergistically. Besides, we propose MHS6 by extending the multi-head design to S6, further enhancing its modeling capability. HydraMamba achieves state-of-the-art results on various tasks at both object-level and scene-level. The code is available at https://github.com/Point-Cloud-Learning/HydraMamba.
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