SES-3D: Beyond Spatial Correspondence for Few-Template 3D Anomaly Localization

Few-template 3D anomaly localization compares a query shape with a small set of normal references. Spatial registration restricts these comparisons to corresponding regions, but leaves a second choice: how should a handful of matched references be scored? We study this choice in a fixed registered pipeline and use Spatial Exemplar Scoring (SES-3D), the cosine distance to the nearest valid same-location normal feature. The score retains observed normal exemplars and requires no local variance estimate. With the encoder, registration, references, and point projection held fixed, it raises point AUROC from 96.36% to 98.54% and point average precision from 46.62% to 63.32% on 1,172 of 1,206 Real3D-AD inputs accepted by registration. It reaches 98.64% point AUROC on the 40 base Anomaly-ShapeNet categories. Three-template comparisons favor exemplar scoring over the raw effectsize statistic on both point metrics; two-template controls show smaller AUROC gains over mean and centroid scoring. Variance shrinkage remains competitive, and estimator preference changes with the representation. These results establish score estimation as a consequential design choice after spatial correspondence, rather than a universally optimal nearest-neighbor rule.