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Towards Generalizable 3D Anomaly Detection via Relational Inconsistency Modeling
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 mode
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