13 papers
MVFM-3DAD: Multi-view Flow Matching for 3D Anomaly Detection via Density Proxy Estimation
Liangwei Li, Lin Liu, Jing Zhang +5
In 3D anomaly detection (3DAD), most existing methods rely on Memory bank retrieval or reconstruction. However, memory-based methods are constrained by the coverage of stored norma…
Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection
Haibo Xiao, Hanzhe Liang, Jie Zhou +2
Detecting anomalies from 3D point clouds has received increasing attention in the field of computer vision, with some group-based or point-based methods achieving impressive result…
Time-reversed Flow Matching with Worst Transport in High-dimensional Latent Space for Image Anomaly Detection
Liangwei Li, Lin Liu, Hanzhe Liang +6
Likelihood-based deep generative models have been widely investigated for Image Anomaly Detection (IAD), particularly Normalizing Flows, yet their strict architectural invertibilit…
Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defects
Hanzhe Liang, Luocheng Zhang, Junyang Xia +7
Although self-supervised 3D anomaly detection assumes that acquiring high-precision point clouds is computationally expensive, in real manufacturing scenarios it is often feasible…
A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Features
Hanzhe Liang, Jie Zhou, Can Gao +3
3D anomaly detection (AD) is a crucial task in computer vision, aiming to identify anomalous points or regions from point cloud data. However, existing methods may encounter challe…
IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detection
Bingyang Guo, Hongjie Li, Ruiyun Yu +2
3D anomaly detection (3D-AD) plays a critical role in industrial manufacturing, particularly in ensuring the reliability and safety of core equipment components. Although existing…