mlsanityData to 14 Oct 2026

Real3D-AD: A Dataset of Point Cloud Anomaly Detection

arXiv 2309.13226 ↗First posted 23 Sep 2023cs.CVcited by 87

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Abstract (arXiv)

High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing. Despite some methodological advances in this area, the scarcity of datasets and the lack of a systematic benchmark hinder its development. We introduce Real3D-AD, a challenging high-precision point cloud anomaly detection dataset, addressing the limitations in the field. With 1,254 high-resolution 3D items from forty thousand to millions of points for each item, Real3D-AD is the largest dataset for high-precision 3D industrial anomaly detection to date. Real3D-AD surpasses existing 3D anomaly detection datasets available regarding point cloud resolution (0.0010mm-0.0015mm), 360 degree coverage and perfect prototype. Additionally, we present a comprehensive benchmark for Real3D-AD, revealing the absence of baseline methods for high-precision point cloud anomaly detection. To address this, we propose Reg3D-AD, a registration-based 3D anomaly detection method incorporating a novel feature memory bank that preserves local and global representations. Extensive experiments on the Real3D-AD dataset highlight the effectiveness of Reg3D-AD. For reproducibility and accessibility, we provide the Real3D-AD dataset, benchmark source code, and Reg3D-AD on our website:https://github.com/M-3LAB/Real3D-AD.

Key facts. Cited by 87 papers in the map (data through 14 Oct 2026). Builds on 23 of its 55 references that are in the map. First posted on arXiv 23 Sep 2023.

Builds on

23 of 55 references.

References · 23

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  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. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  5. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 459
  6. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  7. 2022A Unified Model for Multi-class Anomaly Detectioncited by 268
  8. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 178
  9. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 148
  10. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 139
  11. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 117
  12. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 93
11 more
  1. 2023Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcorecited by 75
  2. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 75
  3. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  4. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 68
  5. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  6. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  7. 2023Segment Any Anomaly without Training via Hybrid Prompt Regularizationcited by 60
  8. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 57
  9. 2023Collaborative Discrepancy Optimization for Reliable Image Anomaly Localizationcited by 57
  10. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  11. 2023What makes a good data augmentation for few-shot unsupervised image anomaly detection?cited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 22; 2025: 34; 2026: 29.

87 papers.

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  1. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 139
  2. 2024AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detectioncited by 127
  3. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  4. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  5. 2025Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Modelscited by 54
  6. 2024R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detectioncited by 45
  7. 2023Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Leadcited by 40
  8. 2024Unsupervised Continual Anomaly Detection with Contrastively-learned Promptcited by 31
  9. 2024PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detectioncited by 30
  10. 2024Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detectioncited by 30
  11. 2024PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detectioncited by 29
  12. 2024MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objectscited by 26
38 more
  1. 2024Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Propertiescited by 26
  2. 2024Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learningcited by 25
  3. 2025MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detectioncited by 23
  4. 2024AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusioncited by 21
  5. 2024Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmarkcited by 16
  6. 2025Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detectioncited by 16
  7. 2024Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Frameworkcited by 15
  8. 2025Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defectscited by 15
  9. 2025Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detectioncited by 12
  10. 2025Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detectioncited by 11
  11. 2025Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detectioncited by 10
  12. 2025Normal-Abnormal Guided Generalist Anomaly Detectioncited by 10
  13. 2024Learning Multi-view Anomaly Detection with Efficient Adaptive Selectioncited by 9
  14. 2025Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representationcited by 9
  15. 2025Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detectioncited by 7
  16. 2024PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Featurescited by 5
  17. 2024Uni-3DAD: GAN-Inversion Aided Universal 3D Anomaly Detection on Model-free Productscited by 5
  18. 2024RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observationscited by 5
  19. 2024DAS3D: Dual-modality Anomaly Synthesis for 3D Anomaly Detectioncited by 5
  20. 2025A Survey on Foundation-Model-Based Industrial Defect Detectioncited by 5
  21. 2025Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detectioncited by 5
  22. 2025SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmarkcited by 5
  23. 2025CASL: Curvature-Augmented Self-supervised Learning for 3D Anomaly Detectioncited by 5
  24. 2025A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Featurescited by 5
  25. 2025Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detectioncited by 4
  26. 2025Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detectioncited by 4
  27. 2025Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmarkcited by 4
  28. 2026GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learningcited by 4
  29. 2024Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Surveycited by 3
  30. 2025C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisorcited by 3
  31. 2025IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detectioncited by 3
  32. 2026A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detectioncited by 3
  33. 2026Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detectioncited by 3
  34. 2025Out-of-Distribution Semantic Occupancy Predictioncited by 2
  35. 20253DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clusteringcited by 2
  36. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  37. 2025IAENet: An Importance-Aware Ensemble Model for 3D Point Cloud-Based Anomaly Detectioncited by 2
  38. 2026Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detectioncited by 2

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