用图谱信息增强Mamba，让点云处理更鲁棒、更精准。
Spectral Informed Mamba for Robust Point Cloud Processing
- 用图拉普拉斯谱定义新遍历顺序，抗视角变化。
- 基于谱成分递归分块，提升分割精度。
- 恢复掩码令牌原位置，保持顺序信息，适合小样本学习。
状态空间模型在自然语言处理和计算机视觉中展现出巨大潜力。本文提出一种融合Mamba与掩码自编码器的新方法，用于点云数据的监督与自监督学习。为增强Mamba对复杂点云结构的处理能力，提出三项关键改进：首先，利用图拉普拉斯矩阵的谱特性捕捉局部连接性，定义了一种保距不变的遍历顺序，相比传统3D网格遍历更鲁棒且能更好刻画形状流形；其次，基于拉普拉斯谱成分设计递归分块策略，实现更精细的特征整合与区域分析；第三，针对掩码自编码器中的令牌位置问题，将恢复原始位置，保留关键顺序信息，提升学习效果。大量实验表明，该方法在分类、分割及少样本任务上均优于现有先进基准。
原文摘要 · Abstract (English)
State space models have shown significant promise in Natural Language Processing (NLP) and, more recently, computer vision. This paper introduces a new methodology leveraging Mamba and Masked Autoencoder networks for point cloud data in both supervised and self-supervised learning. We propose three key contributions to enhance Mamba's capability in processing complex point cloud structures. First, we exploit the spectrum of a graph Laplacian to capture patch connectivity, defining an isometry-invariant traversal order that is robust to viewpoints and better captures shape manifolds than traditional 3D grid-based traversals. Second, we adapt segmentation via a recursive patch partitioning strategy informed by Laplacian spectral components, allowing finer integration and segment analysis. Third, we address token placement in Masked Autoencoder for Mamba by restoring tokens to their original positions, which preserves essential order and improves learning. Extensive experiments demonstrate the improvements of our approach in classification, segmentation, and few-shot tasks over state-of-the-art baselines.
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