AMProp3D: Adaptive Matching with Probability Propagation for 3D Anomaly Detection

Feature matching-based methods have achieved promising performance in industrial point cloud anomaly detection, yet even the same defect type can exhibit sample-dependent feature responses, while different defect patterns activate distinct latent representations and exhibit spatial continuity. Existing methods struggle to capture these variations through sample-adaptive feature matching and neighborhood-aware anomaly scoring, limiting their ability to localize diverse defects. To address this issue, we propose a sample-adaptive point cloud anomaly detection method that jointly enhances sample-relevant feature responses and local consistency of defect responses among neighboring regions. Specifically, Adaptive Kernel Distribution Matching (AKDM) estimates channel relevance from the distribution discrepancies between local test regions and normal references, and incorporates the relevance into an adaptive kernel. Maximum mean discrepancy is then employed to measure distribution-level deviations, enabling feature matching to selectively emphasize latent responses relevant to the current sample. Building on this, Anomaly Probability Surface Propagation (APSP) propagates anomaly evidence among spatially proximal and feature-consistent regions, reinforcing contiguous defect responses while suppressing isolated noise. Experiments on MiniShiftAD, Anomaly-ShapeNet, Real3D-AD, and MulSen-AD demonstrate that the proposed method improves anomaly localization across multiple competitive 3D anomaly detection baselines, validating the effectiveness of sample-adaptive feature matching and spatial consistency modeling.