ZS 3D
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Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detection
arXiv:2603.21511v2 Announce Type: replace Abstract: Zero-shot (ZS) 3D anomaly detection is crucial for reliable industrial inspection, as it enables detecting and localizing defects without requiring any target-category training data. Existing approaches render 3D point clouds into 2D images and leverage pre-trained Vision-Language Models (VLMs) for anomaly detection. However, such strategies inevitably discard geometric details and exhibit limited sensitivity to local anomalies.