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

Detecting and localizing defects in 3D point clouds is chal-lenging because abnormal samples are scarce and diverse, while train-ing is often limited to normal data. We propose Anomaly Factory 3D(AF3AD), a modular framework that synthesizes diverse pseudo-anomaliesfrom normal point clouds to expand the training data for unsupervised3D anomaly detection methods that rely on pseudo-anomalies. AF3ADuses a center-conditioned parametric deformation model defined in localPCA frames, with kernel-controlled spatial falloff, anisotropy, directionalgating, and normal/tangential displacement fields, enabling a broad setof geometric defect presets. We demonstrate its ease-of-use and effec-tiveness by integrating AF3AD with an offset-prediction detector and areconstruction-based anomaly detection method, showing that AF3ADtransfers across detection paradigms. Experiments on AnomalyShapeNetand Real3D-AD show consistent improvements in object- and point-leveldetection and localization, supported by ablations on preset groups androbustness under noise. AF3AD is designed as a standalone synthesis toolto facilitate adoption across different 3D anomaly detection paradigms.Code is available at github.com/vpc-ccg/AF3AD.