6 citations · 10 across the 8 of their papers we have counts for
9 papers · 1 filter
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…
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…
C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor
Haoquan Lu, Hanzhe Liang, Jie Zhang +3
3D Anomaly Detection (AD) has shown great potential in detecting anomalies or defects of high-precision industrial products. However, existing methods are typically trained in a cl…
Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection
Hanzhe Liang, Jie Zhang, Tao Dai +3
Reconstruction-based methods have demonstrated very promising results for 3D anomaly detection. However, these methods face great challenges in handling high-precision point clouds…
Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection
Hanzhe Liang, Aoran Wang, Jie Zhou +3
In this paper, we explore a novel approach to 3D anomaly detection (AD) that goes beyond merely identifying anomalies based on structural characteristics. Our primary perspective i…