Mamba, based on state space model (SSM) with its linear complexity and great
success in classification provide its superiority in 3D point cloud analysis.
Prior to that, Transformer has emerged as one of the most prominent and
successful architectures for point cloud analysis. We present PointABM, a
hybrid model that integrates the Mamba and Transformer architectures for
enhancing local feature to improve performance of 3D point cloud analysis. In
order to enhance the extraction of global features, we introduce a
bidirectional SSM (bi-SSM) framework, which comprises both a traditional token
forward SSM and an innovative backward SSM. To enhance the bi-SSM's capability
of capturing more comprehensive features without disrupting the sequence
relationships required by the bidirectional Mamba, we introduce Transformer,
utilizing its self-attention mechanism to process point clouds. Extensive
experimental results demonstrate that integrating Mamba with Transformer
significantly enhance the model's capability to analysis 3D point cloud.

基于状态空间模型（SSM）的 Mamba 与 Transformer 架构相结合的 PointABM 模型，在改善局部特征以提高 3D 点云分析性能方面表现出优越性，并通过引入双向 SSM 框架和自注意机制进一步增强了特征提取能力，实验证明整合 Mamba 与 Transformer 显著提高了 3D 点云分析模型的能力。