用拓扑分解构建点云区域图，轻量级实现3D点云分类抗噪能力
Mapper-GIN: Lightweight Structural Graph Abstraction for Corrupted 3D Point Cloud Classification
- 基于Mapper算法将点云分块并构建成重叠区域图
- 仅0.5M参数在ModelNet40-C上抗噪声/变换干扰表现稳定
- 结构抽象+GIN消息传递，适合追求高效鲁棒性的研究者
稳健的3D点云分类常依赖于扩大网络规模或专用数据增强。本文提出一个新思路：仅通过结构抽象能否提升鲁棒性？我们基于Mapper算法（主成分分析视角、立方覆盖与密度聚类）对点云进行拓扑分解，将其划分为重叠区域，构建区域图，并使用图同构网络（GIN）进行分类。在模型污染基准ModelNet40-C上，Mapper-GIN仅用0.5M参数，在噪声和变换污染下仍保持良好且稳定的准确率。相比需复杂架构或额外机制的传统方法，该模型通过简单的区域级图抽象与GIN消息传递，实现了强鲁棒性。结果表明，区域图结构为3D视觉识别提供了一种高效且可解释的鲁棒性来源。
原文摘要 · Abstract (English)
Robust 3D point cloud classification is often pursued by scaling up backbones or relying on specialized data augmentation. We instead ask whether structural abstraction alone can improve robustness, and study a simple topology-inspired decomposition based on the Mapper algorithm. We propose Mapper-GIN, a lightweight pipeline that partitions a point cloud into overlapping regions using Mapper (PCA lens, cubical cover, and followed by density-based clustering), constructs a region graph from their overlaps, and performs graph classification with a Graph Isomorphism Network. On the corruption benchmark ModelNet40-C, Mapper-GIN achieves competitive and stable accuracy under Noise and Transformation corruptions with only 0.5M parameters. In contrast to prior approaches that require heavier architectures or additional mechanisms to gain robustness, Mapper-GIN attains strong corruption robustness through simple region-level graph abstraction and GIN message passing. Overall, our results suggest that region-graph structure offers an efficient and interpretable source of robustness for 3D visual recognition.
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