RIFT: Registration-Induced Feature Tuning for Few-Shot 3D Anomaly Localization

Few-shot 3D anomaly localization aims to identify subtle geometric defects from only a few normal reference scans. Existing registration-based methods primarily use alignment to reduce spatial ambiguity during feature matching or representation learning, leaving the structure exposed by registered normal references underexplored after the representation has been fixed. We propose RIFT, a lightweight Registration-Induced Feature Tuning framework that turns these aligned normal references from passive matching memories into supervision for post-hoc target-category specialization. RIFT exploits two complementary cues revealed by registration: canonical coordinates provide spatial identity for a coordinate-conditioned residual update, while same-slot patches across aligned templates provide cross-template structural correspondence supervision. The residual formulation preserves the pretrained representation while adapting its local metric without retraining the encoder or requiring anomalous samples and defect annotations. On Real3D-AD, RIFT improves P-AUROC from 94.21% to 96.43% and P-AUPR from 30.29% to 45.95%. On Anomaly-ShapeNet V2, it improves P-AUROC from 94.65% to 95.10% and P-AUPR from 44.40% to 46.19%. Controlled ablations further show that learned spatial conditioning and correct same-slot correspondence are both important, with the latter providing a clear advantage over generic or shuffled correspondence supervision.