Existing Transformer-based models for point cloud analysis suffer from quadratic complexity, leading to compromised point cloud resolution and information loss. In contrast, the newly proposed Mamba model, based on state space models (SSM), outperforms Transformer in multiple areas with only linear complexity. However, the straightforward adoption of Mamba does not achieve satisfactory performance on point cloud tasks. In this work, we present Mamba3D, a state space model tailored for point cloud learning to enhance local feature extraction, achieving superior performance, high efficiency, and scalability potential. Specifically, we propose a simple yet effective Local Norm Pooling (LNP) block to extract local geometric features. Additionally, to obtain better global features, we introduce a bidirectional SSM (bi-SSM) with both a token forward SSM and a novel backward SSM that operates on the feature channel. Extensive experimental results show that Mamba3D surpasses Transformer-based counterparts and concurrent works in multiple tasks, with or without pre-training. Notably, Mamba3D achieves multiple SoTA, including an overall accuracy of 92.6% (train from scratch) on the ScanObjectNN and 95.1% (with single-modal pre-training) on the ModelNet40 classification task, with only linear complexity.

通过引入局部几何特征提取机制和双向状态空间模型（bi-SSM），Mamba3D模型在点云学习中取得了超过Transformer的卓越性能、高效性和可扩展性，在多个任务中超越同类模型和并行研究，包括在ScanObjectNN任务中从头开始的92.6%综合准确性和在ModelNet40分类任务中基于单模态预训练达到95.1%的准确性，并且具有线性复杂度。

Mamba3D：基于状态空间模型增强3D点云分析的本地特征