mlsanityData to 7 Oct 2026

The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization

arXiv 2112.09045 ↗First posted 16 Dec 2021cs.CVcited by 174

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Abstract (arXiv)

We introduce the first comprehensive 3D dataset for the task of unsupervised anomaly detection and localization. It is inspired by real-world visual inspection scenarios in which a model has to detect various types of defects on manufactured products, even if it is trained only on anomaly-free data. There are defects that manifest themselves as anomalies in the geometric structure of an object. These cause significant deviations in a 3D representation of the data. We employed a high-resolution industrial 3D sensor to acquire depth scans of 10 different object categories. For all object categories, we present a training and validation set, each of which solely consists of scans of anomaly-free samples. The corresponding test sets contain samples showing various defects such as scratches, dents, holes, contaminations, or deformations. Precise ground-truth annotations are provided for every anomalous test sample. An initial benchmark of 3D anomaly detection methods on our dataset indicates a considerable room for improvement.

Key facts. Cited by 174 papers in the map (data through 7 Oct 2026). Builds on 14 of its 41 references that are in the map. First posted on arXiv 16 Dec 2021.

Builds on

14 of 41 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
  3. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  4. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
  5. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
  6. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  7. 20163DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructionscited by 333
  8. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 297
  9. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  10. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  11. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 263
  12. 2017Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesiscited by 215
2 more
  1. 2020BOP Challenge 2020 on 6D Object Localizationcited by 192
  2. 2020Unsupervised 3D Brain Anomaly Detectioncited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 14; 2023: 25; 2024: 41; 2025: 55; 2026: 39.

174 papers.

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  1. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
  2. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  3. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 138
  4. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 138
  5. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 115
  6. 2024MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detectioncited by 113
  7. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 92
  8. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  9. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 74
  10. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  11. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
  12. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
38 more
  1. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  2. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  3. 2023Collaborative Discrepancy Optimization for Reliable Image Anomaly Localizationcited by 56
  4. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 55
  5. 2025Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Modelscited by 54
  6. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
  7. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  8. 2023Multimodal Industrial Anomaly Detection by Crossmodal Feature Mappingcited by 41
  9. 2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
  10. 2023Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulationcited by 33
  11. 2023Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detectioncited by 32
  12. 2024Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detectioncited by 28
  13. 2025The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detectioncited by 28
  14. 2025AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detectioncited by 28
  15. 2025AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detectioncited by 28
  16. 2024SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuningcited by 26
  17. 2024Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Propertiescited by 26
  18. 2024SplatPose & Detect: Pose-Agnostic 3D Anomaly Detectioncited by 25
  19. 2024Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learningcited by 25
  20. 2024M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoisingcited by 24
  21. 2024ResAD: A Simple Framework for Class Generalizable Anomaly Detectioncited by 24
  22. 2025MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detectioncited by 23
  23. 2023Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstructioncited by 22
  24. 2023Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrievalcited by 22
  25. 2022MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detectioncited by 19
  26. 2024Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoningcited by 19
  27. 2022Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detectioncited by 18
  28. 2024Self-supervised Feature Adaptation for 3D Industrial Anomaly Detectioncited by 16
  29. 2024CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generationcited by 16
  30. 2025Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detectioncited by 16
  31. 2024Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmarkcited by 15
  32. 2024Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Frameworkcited by 15
  33. 2024Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detectioncited by 15
  34. 2022Anomaly Detection Requires Better Representationscited by 14
  35. 2023Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detectioncited by 14
  36. 2024VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detectioncited by 13
  37. 2022AFSC: Adaptive Fourier Space Compression for Anomaly Detectioncited by 12
  38. 2022Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samplescited by 12

50 most cited of 174 All 174 (JSON)