用新型图结构让点云分析更高效可解释
A Unified Non-Parametric and Interpretable Point Cloud Analysis via t-FCW Graph Representation
- 提出t-FCW图表示，将点云映射到度量空间
- 在ModelNet40上7秒完成分类，速度极快
- 适合需要可解释性的点云任务开发者
我们引入增强版转置全连接加权（empowered t-FCW）图表示，将点云嵌入度量空间。尽管原始t-FCW已在点云分类中表现优异，但其有效性及适用范围仍不明确。本文分析了增强版与原始t-FCW的有效性来源，并设计仅使用增强t-FCW作为特征提取器的网络。从可解释性角度，基于增强t-FCW构建了用于分类、部件分割和语义分割的记忆库。分析表明，增强t-FCW继承了表面描述子的鲁棒性，并通过维度间关系实现可解释性。该特性使网络高效且可解释，在NVIDIA RTX A5000 GPU上处理ModelNet40分类任务约耗时7秒。重要的是，增强t-FCW既可作为轻量级独立基线，也可作为现有深度模型的互补插件。
原文摘要 · Abstract (English)
We introduce an empowered transposed Fully Connected Weighted (t-FCW) graph representation to embed point clouds into a metric space. While original t-FCW has shown promising results for point cloud classification, the reasons behind its effectiveness and its broader applicability remained unclear. In this work, we analyze the properties that make the empowered and original t-FCW effective and design a network that uses the empowered t-FCW exclusively as feature extractors. From an interpretability perspective, we build memory banks for classification, part segmentation, and semantic segmentation using the empowered t-FCW. Our analysis reveals that the empowered t-FCW inherits robustness from surface descriptors, provides interpretability through dimension-wise relations. These properties enable a highly efficient and interpretable network, which processes the ModelNet40 classification problem in approximately 7 seconds on an NVIDIA RTX A5000 GPU. Importantly, empowered t-FCW can function both as a lightweight standalone baseline and as a complementary plug-in to existing deep models.
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