3DzAL performs zero-shot 3D anomaly localization by combining contrastive patch learning, a normalcy classifier, and adversarial perturbation on pseudo-anomalies generated from task-irrelevant point clouds.
Anomaly detection in 3D point clouds using deep geometric descriptors
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Towards Zero-shot 3D Anomaly Localization
3DzAL performs zero-shot 3D anomaly localization by combining contrastive patch learning, a normalcy classifier, and adversarial perturbation on pseudo-anomalies generated from task-irrelevant point clouds.