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cs.CV2026
Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detection
Kaiqiang Li, Gang Li, Mingle Zhou +3
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 dat…
cs.CV2026
Multimodal Industrial Anomaly Detection via Geometric Prior
Min Li, Jinghui He, Gang Li +3
The purpose of multimodal industrial anomaly detection is to detect complex geometric shape defects such as subtle surface deformations and irregular contours that are difficult to…
cs.CV2025
MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detection
Gang Li, Tianjiao Chen, Mingle Zhou +3
Zero-shot 3D (ZS-3D) anomaly detection aims to identify defects in 3D objects without relying on labeled training data, making it especially valuable in scenarios constrained by da…