MC4AD predicts per-point internal and external corrective force vectors from point clouds and uses their magnitude as an anomaly score, reporting state-of-the-art detection and segmentation on five benchmarks plus a new synthetic dataset.
Towards scalable 3d anomaly detection and localization: A benchmark via 3d anomaly synthesis and a self-supervised learning network,
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Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection
MC4AD predicts per-point internal and external corrective force vectors from point clouds and uses their magnitude as an anomaly score, reporting state-of-the-art detection and segmentation on five benchmarks plus a new synthetic dataset.