用深度学习自动识别点云拓扑图中的关键点
TUN: Detecting Significant Points in Persistence Diagrams with Deep Learning
- 融合增强描述子与自注意力机制，结合点云编码器进行多模态建模
- 在真实数据上显著优于传统方法，提升关键点检测准确率
- 适合需要自动化拓扑分析的科研与工业场景
持久性图（PDs）是理解点云底层形状拓扑结构的强大工具，但如何识别其中蕴含真实信号的点仍具挑战，制约了拓扑数据分析在实际应用中的推广。本文针对一维持久性图提出拓扑理解网络（TUN），一种结合增强型PD描述子、自注意力机制、PointNet风格点云编码器、可学习融合与逐点分类的多模态网络，并采用稳定预处理与不平衡感知训练策略。该方法能自动有效识别持久性图中的显著点，对下游应用至关重要。实验表明，TUN在真实数据上显著优于经典方法，验证了其在实际场景中的有效性。
原文摘要 · Abstract (English)
Persistence diagrams (PDs) provide a powerful tool for understanding the topology of the underlying shape of a point cloud. However, identifying which points in PDs encode genuine signals remains challenging. This challenge directly hinders the practical adoption of topological data analysis in many applications, where automated and reliable interpretation of persistence diagrams is essential for downstream decision-making. In this paper, we study automatic significance detection for one-dimensional persistence diagrams. Specifically, we propose Topology Understanding Net (TUN), a multi-modal network that combines enhanced PD descriptors with self-attention, a PointNet-style point cloud encoder, learned fusion, and per-point classification, alongside stable preprocessing and imbalance-aware training. It provides an automated and effective solution for identifying significant points in PDs, which are critical for downstream applications. Experiments show that TUN outperforms classic methods in detecting significant points in PDs, illustrating its effectiveness in real-world applications.
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