3D-SONAR: Self-Organizing Network for 3D Anomaly Ranking

TL;DR

This paper introduces 3D-SONAR, a physics-inspired, unsupervised method for surface anomaly detection in 3D point clouds that does not require training data, offering a new approach for industrial inspection.

Contribution

The paper presents a novel self-organizing network model for 3D anomaly ranking that leverages force interactions in a graph to detect surface anomalies without training.

Findings

Achieves superior anomaly detection performance without training.

Effective on both open and closed surface datasets.

Highlights potential of physics-inspired models in unsupervised inspection.

Abstract

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.