Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection

2026-06-27

AI Summary

This work addresses the challenge of 3D point cloud anomaly detection, where the scarcity and diversity of anomalous samples typically restrict training to normal data alone, thereby limiting model generalization. To overcome this, the authors propose a modular framework that enhances unsupervised training by synthesizing diverse pseudo-anomalies. Specifically, they construct a parametric deformation model based on local PCA coordinate systems, enabling anisotropic, direction-gated, and normal/tangential displacement fields to generate a rich variety of geometric defects. The approach is highly versatile, compatible with both reconstruction- and offset-prediction-based detection paradigms. Experiments on AnomalyShapeNet and Real3D-AD demonstrate significant improvements in both object-level and point-level anomaly detection and localization performance, while ablation studies confirm the effectiveness of individual components and robustness to noise.

Abstract

Detecting and localizing defects in 3D point clouds is challenging because abnormal samples are scarce and diverse, while training is often limited to normal data. We propose Anomaly Factory 3D (AF3AD), a modular framework that synthesizes diverse pseudo-anomalies from normal point clouds to expand the training data for unsupervised 3D anomaly detection methods that rely on pseudo-anomalies. AF3AD uses a center-conditioned parametric deformation model defined in local PCA frames, with kernel-controlled spatial falloff, anisotropy, directional gating, and normal/tangential displacement fields, enabling a broad set of geometric defect presets. We demonstrate its ease-of-use and effectiveness by integrating AF3AD with an offset-prediction detector and a reconstruction-based anomaly detection method, showing that AF3AD transfers across detection paradigms. Experiments on AnomalyShapeNet and Real3D-AD show consistent improvements in object- and point-level detection and localization, supported by ablations on preset groups and robustness under noise. AF3AD is designed as a standalone synthesis tool to facilitate adoption across different 3D anomaly detection paradigms. Code is available at github.com/vpc-ccg/AF3AD.

Problem

• 3D anomaly detection
• pseudo-anomaly synthesis
• point cloud
• unsupervised learning
• defect localization

Innovation

• pseudo-anomaly synthesis
• modular framework
• parametric deformation model
• unsupervised 3D anomaly detection
• geometric defect presets