Transformers have become one of the foundational architectures in point cloud
analysis tasks due to their excellent global modeling ability. However, the
attention mechanism has quadratic complexity and is difficult to extend to long
sequence modeling due to limited computational resources and so on. Recently,
state space models (SSM), a new family of deep sequence models, have presented
great potential for sequence modeling in NLP tasks. In this paper, taking
inspiration from the success of SSM in NLP, we propose PointMamba, a framework
with global modeling and linear complexity. Specifically, by taking embedded
point patches as input, we proposed a reordering strategy to enhance SSM's
global modeling ability by providing a more logical geometric scanning order.
The reordered point tokens are then sent to a series of Mamba blocks to
causally capture the point cloud structure. Experimental results show our
proposed PointMamba outperforms the transformer-based counterparts on different
point cloud analysis datasets, while significantly saving about 44.3%
parameters and 25% FLOPs, demonstrating the potential option for constructing
foundational 3D vision models. We hope our PointMamba can provide a new
perspective for point cloud analysis. The code is available at
this https URL

基于状态空间模型 (SSM) 成功在 NLP 任务中进行序列建模的启发，我们提出了具有全局建模和线性复杂度的 PointMamba 框架，通过重新排序策略提供更合理的几何扫描顺序来增强 SSM 的全局建模能力，实验证明我们的 PointMamba 在不同的点云分析数据集上胜过基于 transformer 的对应模型，同时节省了约 44.3% 的参数和 25% 的 FLOPs，展示了构建基础 3D 视觉模型的潜在选择，并为点云分析提供了新的视角。