ANYNORMAL: LET EVERY POINT CHOOSE ITS NORMAL

In few-shot 3D anomaly detection, only a handful of normal examples are available to characterize normality, even though normal objects can differ in local shape and sampling. This raises two questions: how should a geometric difference be judged against the variation among normal objects, and which normal example should be used to assess each test region? We propose AnyNormal. It first compares the normal examples with one another to estimate the range of normal geometric variation. At test time, each query point is matched independently to every aligned normal reference. For each reference, Euclidean, point-to-plane, and normal-angle differences are measured relative to the variation observed among normal examples, at both the point and local-neighborhood levels, and averaged into a score. The minimum score across references is used for each point, allowing different regions of the same object to be assessed against different normal examples. No scoring network is trained: a parameter-free mean of these calibrated geometric features achieves 90.3/95.1%, 94.5/96.2%, and 89.0/87.0% object/point AUROC on Real3D-AD, Anomaly-ShapeNet, and MulSen-AD, respectively. In a transfer experiment, a learned scorer trained on only four normal scans from a single Real3D-AD category reaches essentially the same accuracy across all three datasets without further weight updates. A compressed configuration achieves 85.9/82.4%, 90.1/88.0%, and 79.3/71.4% object/point AUROC on the three datasets, respectively, while reaching 69.74 FPS in continuous-stream evaluation on an RTX 4090. Code for the parameter-free method is available at https://anonymous.4open.science/r/AnyNormal/.