UniNorm: Normal-Direction Surface-Deviation Measurement for Multi-Category 3D Anomaly Detection

Multi-category 3D anomaly detection must detect and localize defects across heterogeneous categories using only normal training data. Existing methods largely improve representations of normality while inheriting feature distance, reconstruction error, or field residual as anomaly evidence, coupling representation quality with the suitability of the resulting measurement. Under matched registered inputs and identical object pooling, we compare geometric residuals with learned implicit-field residuals and find performance differences. We therefore introduce UniNorm, which formulates template-aligned 3D anomaly detection as normal-direction surface-deviation measurement. UniNorm uses absolute normal projection to retain surface-normal departure while suppressing tangential correspondence motion, and optionally applies a nonparametric normal-dispersion correction (NULB) together with a shared contrast-tail object readout. The scorer has no learned parameters; only a lightweight category-specific nominal template is stored. On Anomaly-ShapeNet and Real3D-AD, UniNorm achieves 92.60/90.53% and 85.14/83.48% object-/point-level AUROC, respectively. With registration and object pooling matched, absolute normal projection improves over a class-conditioned implicit-field residual by 6.83/8.95 and 3.16/8.26 O/PAUROC points on the two benchmarks. These results highlight anomaly measurement as a first-class design choice in template aligned 3D anomaly detection.