1 citations · 3 across the 9 of their papers we have counts for
10 papers
MATERO-RCA: Mode-Aware Trajectory-Level Energy-Based Root-Set Optimization for Industrial Root Cause Analysis
Chengyu Tao, Chunxi Huang, Runquan Xiao
Root cause analysis (RCA) for contextual anomalies in industrial time series is challenging because responses depend jointly on control commands, operating states, and coupled phys…
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…
FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios
Xiangru Jian, Hao Xu, Wei Pang +13
The manufacturing sector is increasingly adopting Multimodal Large Language Models (MLLMs) to transition from simple perception to autonomous execution, yet current evaluations fai…
SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection
Letian Bai, Chengyu Tao, Juan Du
Multi-view anomaly detection aims to identify surface defects on complex objects using observations captured from multiple viewpoints. However, existing unsupervised methods often…
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…
A Novel Representation of Periodic Pattern and Its Application to Untrained Anomaly Detection
Peng Ye, Chengyu Tao, Juan Du
There are a variety of industrial products that possess periodic textures or surfaces, such as carbon fiber textiles and display panels. Traditional image-based quality inspection…
F2PAD: A General Optimization Framework for Feature-Level to Pixel-Level Anomaly Detection
Chengyu Tao, Hao Xu, Juan Du
Image-based inspection systems have been widely deployed in manufacturing production lines. Due to the scarcity of defective samples, unsupervised anomaly detection that only lever…
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…