Cover Image

IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Guo, Bingyang, Li, Hongjie, Yu, Ruiyun, Liang, Hanzhe, Wang, Jinbao
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defects
by: Liang, Hanzhe, et al.
Published: (2026)

A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Features
by: Liang, Hanzhe, et al.
Published: (2025)

Mentor3AD: Feature Reconstruction-based 3D Anomaly Detection via Multi-modality Mentor Learning
by: Liang, Hanzhe
Published: (2025)

Point Cloud Segmentation of Integrated Circuits Package Substrates Surface Defects Using Causal Inference: Dataset Construction and Methodology
by: Guo, Bingyang, et al.
Published: (2025)

C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor
by: Lu, Haoquan, et al.
Published: (2025)

Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection
by: Liang, Hanzhe, et al.
Published: (2025)

Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection
by: Xiao, Haibo, et al.
Published: (2026)

Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detection
by: Liang, Hanzhe, et al.
Published: (2025)

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection
by: Bai, Haoyu, et al.
Published: (2025)

Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection
by: Liang, Hanzhe, et al.
Published: (2024)

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection
by: Ye, Jianan, et al.
Published: (2024)

3DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clustering
by: Wang, Zi, et al.
Published: (2025)

MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection
by: Cheng, Jiayi, et al.
Published: (2025)

Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection
by: Liang, Hanzhe, et al.
Published: (2025)

Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection
by: Tong, Xuan, et al.
Published: (2025)

PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection
by: Zhou, Qihang, et al.
Published: (2024)

Multi-Modal Assistance for Unsupervised Domain Adaptation on Point Cloud 3D Object Detection
by: Zhao, Shenao, et al.
Published: (2025)

PointAD+: Learning Hierarchical Representations for Zero-shot 3D Anomaly Detection
by: Zhou, Qihang, et al.
Published: (2025)

Industrial3D: A Terrestrial LiDAR Point Cloud Dataset and CrossParadigm Benchmark for Industrial Infrastructure
by: Yin, Chao, et al.
Published: (2026)

A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Image Anomaly Detection
by: Lin, Yuxuan, et al.
Published: (2024)

The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection
by: Heckler-Kram, Lars, et al.
Published: (2025)

R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection
by: Zhou, Zheyuan, et al.
Published: (2024)

Unsupervised UAV 3D Trajectories Estimation with Sparse Point Clouds
by: Liang, Hanfang, et al.
Published: (2024)

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection
by: Zhu, Wenbing, et al.
Published: (2025)

Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection
by: Tu, Yuanpeng, et al.
Published: (2024)

Key-Grid: Unsupervised 3D Keypoints Detection using Grid Heatmap Features
by: Hou, Chengkai, et al.
Published: (2024)

PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation
by: Zhang, Jian, et al.
Published: (2023)

PointGS: Semantic-Consistent Unsupervised 3D Point Cloud Segmentation with 3D Gaussian Splatting
by: Song, Yixiao, et al.
Published: (2026)

CSAD: Unsupervised Component Segmentation for Logical Anomaly Detection
by: Hsieh, Yu-Hsuan, et al.
Published: (2024)

IAENet: An Importance-Aware Ensemble Model for 3D Point Cloud-Based Anomaly Detection
by: Cao, Xuanming, et al.
Published: (2025)

Noise2Score3D: Tweedie's Approach for Unsupervised Point Cloud Denoising
by: Wei, Xiangbin, et al.
Published: (2025)

Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detection
by: Sun, Yihan, et al.
Published: (2026)

VoxelTrack: Exploring Voxel Representation for 3D Point Cloud Object Tracking
by: Lu, Yuxuan, et al.
Published: (2024)

Deep Industrial Image Anomaly Detection: A Survey
by: Liu, Jiaqi, et al.
Published: (2023)

Unsupervised Change Detection for Space Habitats Using 3D Point Clouds
by: Santos, Jamie, et al.
Published: (2023)

Point Cloud Unsupervised Pre-training via 3D Gaussian Splatting
by: Liu, Hao, et al.
Published: (2024)

Leveraging Single-View Images for Unsupervised 3D Point Cloud Completion
by: Wu, Lintai, et al.
Published: (2022)

Resolving Long-Tail Ambiguity in Unsupervised 3D Point Cloud Segmentation with Language Priors
by: Wei, Siqi, et al.
Published: (2026)

STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset
by: Chen, Meida, et al.
Published: (2022)

ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection
by: Liang, Yun, et al.
Published: (2024)