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arXiv · 2609.35059

Towards Generalizable 3D Anomaly Detection via Relational Inconsistency Modeling

KunHo Heo·SuYeon Kim·Hayoung Lee·Chanse Oh·MyeongAh Cho
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Abstract

3D anomaly detection (3DAD) aims to identify defective regions in point cloud data, serving as a critical component in industrial inspection systems. Existing methods are normality-centered -- learning the distribution of normal samples and treating deviations as anomalies -- without explicitly modeling what constitutes a defect. This leads to ambiguous decision boundaries with increased false positives and negatives, particularly in unified and cross-domain settings where diverse normal distributions further blur the boundaries. We propose a relational inconsistency modeling framework that characterizes defects as violations of geometric consistency among neighboring structures. Our approach learns category-agnostic defect cues through pseudo-anomalies designed as controlled relational violations, instantiated by two key modules: Edge-aware Graph Refinement (EGR) for encoding geometric relationships among local regions, and Cluster-Deviation Modeling (CDM) for identifying regions that are relationally incompatible within their structural peer group. Extensive experiments on Anomaly-ShapeNet and Real3D-AD demonstrate consistent improvements over prior state-of-the-art methods in both in-domain and cross-domain settings, validating the effectiveness of learning an explicit, relation-based defect criterion for 3D anomaly detection. Project page: https://visualsciencelab-khu.github.io/GRIM_project/.

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KunHo Heo, SuYeon Kim, Hayoung Lee, Chanse Oh, MyeongAh Cho. 2026-09-28. Towards Generalizable 3D Anomaly Detection via Relational Inconsistency Modeling. https://arxiv.org/abs/2609.35059

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