3 citations · 4 across the 12 of their papers we have counts for
12 papers · 1 filter
PC-AD: Point Cloud Upsampling to Safeguard 3D Anomaly Detection with Resolution-constrained Edge Devices
Yutong Gu, Yingxi Xie, Kejin Huang +4
Low-cost and low-resolution sensors used in edge deployments can produce test point clouds that are substantially sparser than the normal training data. This train-test sampling-re…
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…
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…
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…
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…
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…
Mentor3AD: Feature Reconstruction-based 3D Anomaly Detection via Multi-modality Mentor Learning
Hanzhe Liang
Multimodal feature reconstruction is a promising approach for 3D anomaly detection, leveraging the complementary information from dual modalities. We further advance this paradigm…
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…
Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detection
Hanzhe Liang, Jie Zhou, Xuanxin Chen +3
3D anomaly detection (AD) is prominent but difficult due to lacking a unified theoretical foundation for preprocessing design. We establish the Fence Theorem, formalizing preproces…
Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection
Hanzhe Liang, Guoyang Xie, Chengbin Hou +3
3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typi…