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

• URL: http://arxiv.org/abs/2511.03267v1
• Date: Wed, 05 Nov 2025 08:01:23 GMT
• Title: IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detection
• Authors: Bingyang Guo, Hongjie Li, Ruiyun Yu, Hanzhe Liang, Jinbao Wang,
• Abstract summary: 3D anomaly detection (3D-AD) plays a critical role in industrial manufacturing, particularly in ensuring the reliability and safety of core equipment components.<n>Existing 3D datasets like Real3D-AD and MVTec 3D-AD offer broad application support, but fall short in capturing the complexities and subtle defects found in real industrial environments.<n>We have developed a point cloud anomaly detection dataset ( IEC3D-AD) specific to real industrial scenarios.<n>This dataset is directly collected from actual production lines, ensuring high fidelity and relevance.
• Score: 16.60482902001866
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 3D datasets like Real3D-AD and MVTec 3D-AD offer broad application support, they fall short in capturing the complexities and subtle defects found in real industrial environments. This limitation hampers precise anomaly detection research, especially for industrial equipment components (IEC) such as bearings, rings, and bolts. To address this challenge, we have developed a point cloud anomaly detection dataset (IEC3D-AD) specific to real industrial scenarios. This dataset is directly collected from actual production lines, ensuring high fidelity and relevance. Compared to existing datasets, IEC3D-AD features significantly improved point cloud resolution and defect annotation granularity, facilitating more demanding anomaly detection tasks. Furthermore, inspired by generative 2D-AD methods, we introduce a novel 3D-AD paradigm (GMANet) on IEC3D-AD. This paradigm generates synthetic point cloud samples based on geometric morphological analysis, then reduces the margin and increases the overlap between normal and abnormal point-level features through spatial discrepancy optimization. Extensive experiments demonstrate the effectiveness of our method on both IEC3D-AD and other datasets.

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