Surface anomaly detection using 3D point cloud data has gained increasing attention in industrial inspection. However, most existing methods rely on deep learning techniques that are highly dependent on large-scale datasets for training, which are difficult and expensive to acquire in real-world applications. To address this challenge, we propose a novel method based on self-organizing network for 3D anomaly ranking, also named 3D-SONAR. The core idea is to model the 3D point cloud as a dynamic system, where the points are represented as an undirected graph and interact via attractive and repulsive forces. The energy distribution induced by these forces can reveal surface anomalies. Experimental results show that our method achieves superior anomaly detection performance in both open surface and closed surface without training. This work provides a new perspective on unsupervised inspection and highlights the potential of physics-inspired models in industrial anomaly detection tasks with limited data.

翻译：利用三维点云数据进行表面异常检测在工业检测领域日益受到关注。然而，现有方法大多依赖于深度学习技术，这些技术高度依赖大规模数据集进行训练，而在实际应用中获取此类数据既困难又昂贵。为应对这一挑战，我们提出了一种基于自组织网络的三维异常排序新方法，亦称为3D-SONAR。其核心思想是将三维点云建模为一个动态系统，其中点表示为无向图，并通过吸引力和排斥力相互作用。这些力诱导的能量分布可揭示表面异常。实验结果表明，我们的方法在无需训练的情况下，对开放表面和封闭表面均实现了优异的异常检测性能。这项工作为无监督检测提供了新视角，并凸显了受物理学启发的模型在数据有限的工业异常检测任务中的潜力。