5 papers
Align3D-AD: Cross-Modal Feature Alignment and Dual-Prompt Learning for Zero-shot 3D Anomaly Detection
Letian Bai, Xuanming Cao, Juan Du +1
Zero-shot 3D anomaly detection aims to identify anomalies without access to training data from target categories. However, existing methods mainly rely on projecting 3D observation…
IAENet: An Importance-Aware Ensemble Model for 3D Point Cloud-Based Anomaly Detection
Xuanming Cao, Chengyu Tao, Yifeng Cheng +1
Surface anomaly detection is pivotal for ensuring product quality in industrial manufacturing. While 2D image-based methods have achieved remarkable success, 3D point cloud-based d…
GSF-MIAD: Geometry-Guided Score Fusion for Multimodal Industrial Anomaly Detection
Chengyu Tao, Xuanming Cao, Juan Du
Industrial quality inspection plays a critical role in modern manufacturing by identifying defective products during production. While single-modality approaches using either 3D po…
Deep Subspace Learning for Surface Anomaly Classification Based on 3D Point Cloud Data
Xuanming Cao, Chengyu Tao, Juan Du
Surface anomaly classification is critical for manufacturing system fault diagnosis and quality control. However, the following challenges always hinder accurate anomaly classifica…
3D-CSAD: Untrained 3D Anomaly Detection for Complex Manufacturing Surfaces
Xuanming Cao, Chengyu Tao, Juan Du
The surface quality inspection of manufacturing parts based on 3D point cloud data has attracted increasing attention in recent years. The reason is that the 3D point cloud can cap…