Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detection

2026-07-11

AI Summary

This work addresses the challenge of 3D point cloud anomaly detection, where the scarcity of real anomalous samples and high annotation costs hinder effective learning of discriminative features. To overcome this limitation, the authors propose the PA3AD framework, which generates diverse and plausible pseudo-anomalies from normal data through multi-physical modeling. The framework further incorporates a momentum-updated prototype mechanism and a discrepancy-aware fusion module to jointly guide the model in capturing distributional shifts between normal and anomalous patterns. By integrating these components with weight-shared 3D representation learning, PA3AD achieves state-of-the-art performance on both Anomaly-ShapeNet and Real3D-AD benchmarks, significantly outperforming existing methods.

Abstract

3D point cloud anomaly detection plays a vital role in industrial manufacturing, yet it faces significant challenges due to the scarcity and high acquisition cost of real anomalous samples. The inherently anomaly-free training data further hinders detection methods from effectively learning discriminative features between normal and abnormal instances. To address these issues, we propose PA3AD, a novel framework that introduces a physics-inspired pseudo-anomaly generation strategy to create physically plausible anomalous samples from normal data. Additionally, we incorporate prototype features via a weight-sharing mechanism to guide the model in capturing the distribution shifts between normal and anomalous samples. Specifically, PA3AD introduces two key innovations to tackle the scarcity of real anomalies. First, a physics-inspired module generates diverse pseudo-anomalous point clouds from normal data via multi-physics modeling. Second, momentum-updated prototypes and a difference-aware fusion block capture stable normal representations and their discrepancies with pseudo-anomalies. This design effectively learns distribution shifts, achieving superior detection performance. Extensive experiments on the Anomaly-ShapeNet and Real3D-AD datasets demonstrate that our method consistently outperforms existing state-of-the-art approaches. Our code will be made publicly available at https://github.com/NingxiaoJian/PA3AD.

Problem

• 3D anomaly detection
• anomaly scarcity
• point cloud
• industrial manufacturing
• anomaly-free training

Innovation

• pseudo-anomaly generation
• physics-inspired modeling
• prototype feature guidance
• 3D point cloud anomaly detection
• distribution shift learning