FROM VARIATION TO VIOLATION: LEARNING 3D ANOMALY SHAPES FROM NORMAL SURFACES

Existing 3D point cloud anomaly synthesis methods largely rely on hand-crafted deformation operators or predefined anomaly morphologies, limiting the diversity of synthesized anomalies. We observe that local changes such as bulges and depressions can also occur naturally across normal instances, and may become anomalous when they become stronger than what is normally observed. Based on this observation, we propose NOVA, a variation-driven framework for 3D anomaly synthesis and detection. NOVA uses local surface differences among aligned normal instances as deformation patterns and strengthens them beyond normal variation to create pseudo-anomalies. For detection, a normal-geometry branch first learns how much and in which directions normal surfaces vary, and is then frozen. Using its geometric features and reference-matching statistics, an anomaly-scoring branch learns from paired normal and deformed samples to assign higher scores to the same points after deformation. The resulting scores support anomaly detection and localization. Experiments confirm more diverse synthesized anomaly patterns. The normal-geometry branch alone surpasses the best published O-AUROC and P-AUROC results on both datasets using only normal training data. Supervision from 32 pseudo-anomalies per category further improves performance, raising P-AUROC from 92.68% to 95.99% on Real3D-AD and from 96.94% to 97.16% on Anomaly-ShapeNet. The resulting framework achieves O-AUROC scores of 91.28% and 96.37%, respectively. Code: https://anonymous.4open.science/r/NOVA-7C92/.