mlsanityData to 14 Oct 2026

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

arXiv 2511.03267 ↗First posted 5 Nov 2025cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 30 of its 48 references that are in the map. First posted on arXiv 5 Nov 2025.

Builds on

30 of 48 references.

References · 30

  1. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  2. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  3. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 653
  4. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 178
  5. 2024PointMamba: A Simple State Space Model for Point Cloud Analysiscited by 167
  6. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 148
  7. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 139
  8. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 117
  9. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 93
  10. 2019Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifierscited by 92
  11. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  12. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 75
18 more
  1. 2024Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategycited by 73
  2. 2020Fast and Robust Iterative Closest Pointcited by 67
  3. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  4. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  5. 2024Point Cloud Mamba: Point Cloud Learning via State Space Modelcited by 66
  6. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 57
  7. 2024Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Modelcited by 57
  8. 2024R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detectioncited by 45
  9. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  10. 2023Multimodal Industrial Anomaly Detection by Crossmodal Feature Mappingcited by 42
  11. 2024Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detectioncited by 30
  12. 2024PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysiscited by 26
  13. 2024Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Propertiescited by 26
  14. 2024SplatPose & Detect: Pose-Agnostic 3D Anomaly Detectioncited by 25
  15. 2025MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detectioncited by 23
  16. 2023Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detectioncited by 21
  17. 2025Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detectioncited by 11
  18. 2024Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmarkcited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defectscited by 0
  2. 2026A Synthetic 3D Gear Dataset for Manufacturing Quality Inspection (MFGNet-Gear)cited by 0
  3. 2026Beyond Geometry: Benchmarking and Consistency Reasoning for 3D Logical Anomaly Detectioncited by 0