mlsanityData to 14 Oct 2026

Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors

arXiv 2202.11660 ↗First posted 23 Feb 2022cs.CVcited by 66

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Abstract (arXiv)

We present a new method for the unsupervised detection of geometric anomalies in high-resolution 3D point clouds. In particular, we propose an adaptation of the established student-teacher anomaly detection framework to three dimensions. A student network is trained to match the output of a pretrained teacher network on anomaly-free point clouds. When applied to test data, regression errors between the teacher and the student allow reliable localization of anomalous structures. To construct an expressive teacher network that extracts dense local geometric descriptors, we introduce a novel self-supervised pretraining strategy. The teacher is trained by reconstructing local receptive fields and does not require annotations. Extensive experiments on the comprehensive MVTec 3D Anomaly Detection dataset highlight the effectiveness of our approach, which outperforms the next-best method by a large margin. Ablation studies show that our approach meets the requirements of practical applications regarding performance, runtime, and memory consumption.

Key facts. Cited by 66 papers in the map (data through 14 Oct 2026). Builds on 22 of its 53 references that are in the map. First posted on arXiv 23 Feb 2022.

Builds on

22 of 53 references.

References · 22

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  3. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 585
  4. 2020PointContrast: Unsupervised Pre-training for 3D Point Cloud Understandingcited by 504
  5. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  6. 20163DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructionscited by 336
  7. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  8. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 300
  9. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  10. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 264
  11. 2018PPFNet: Global Context Aware Local Features for Robust 3D Point Matchingcited by 181
  12. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 178
10 more
  1. 2020PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentationcited by 175
  2. 2018Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 146
  3. 2018PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptorscited by 141
  4. 2019Towards Visually Explaining Variational Autoencoderscited by 113
  5. 2021Learning Semantic Segmentation of Large-Scale Point Clouds with Random Samplingcited by 51
  6. 2016Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimationcited by 34
  7. 2020Unsupervised 3D Brain Anomaly Detectioncited by 12
  8. 2018Image Anomalies: a Review and Synthesis of Detection Methodscited by 10
  9. 2021Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representationscited by 10
  10. 2020Combining GANs and AutoEncoders for Efficient Anomaly Detectioncited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 15; 2024: 19; 2025: 18; 2026: 12.

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  1. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 148
  2. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 139
  3. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 93
  4. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  5. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 75
  6. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  7. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  8. 2023Collaborative Discrepancy Optimization for Reliable Image Anomaly Localizationcited by 57
  9. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  10. 2023Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Leadcited by 40
  11. 2023Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulationcited by 33
  12. 2024PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detectioncited by 30
38 more
  1. 2024Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detectioncited by 30
  2. 2024PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detectioncited by 29
  3. 2024Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learningcited by 25
  4. 2024M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoisingcited by 24
  5. 2025MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detectioncited by 23
  6. 2024Self-supervised Feature Adaptation for 3D Industrial Anomaly Detectioncited by 16
  7. 2024CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generationcited by 16
  8. 2025Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detectioncited by 16
  9. 2024Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Frameworkcited by 15
  10. 2022Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samplescited by 12
  11. 2024Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspectivecited by 12
  12. 2025Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detectioncited by 12
  13. 2025Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representationcited by 9
  14. 2024Towards Zero-shot 3D Anomaly Localizationcited by 8
  15. 2024Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillationcited by 6
  16. 2023STARNet: Sensor Trustworthiness and Anomaly Recognition via Approximated Likelihood Regret for Robust Edge Autonomycited by 5
  17. 2024Uni-3DAD: GAN-Inversion Aided Universal 3D Anomaly Detection on Model-free Productscited by 5
  18. 2024DAS3D: Dual-modality Anomaly Synthesis for 3D Anomaly Detectioncited by 5
  19. 2026GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learningcited by 4
  20. 2023That's BAD: Blind Anomaly Detection by Implicit Local Feature Clusteringcited by 3
  21. 2023Multi-Scale Distillation for RGB-D Anomaly Detection on the PD-REAL Datasetcited by 3
  22. 2024Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matchingcited by 3
  23. 2025C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisorcited by 3
  24. 2025IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detectioncited by 3
  25. 2026Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detectioncited by 3
  26. 20243D-CSAD: Untrained 3D Anomaly Detection for Complex Manufacturing Surfacescited by 2
  27. 2025Out-of-Distribution Semantic Occupancy Predictioncited by 2
  28. 20253DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clusteringcited by 2
  29. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  30. 2025IAENet: An Importance-Aware Ensemble Model for 3D Point Cloud-Based Anomaly Detectioncited by 2
  31. 2025MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detectioncited by 2
  32. 2023Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encodercited by 1
  33. 2023Representation Learning in Anomaly Detection: Successes, Limits and a Grand Challengecited by 1
  34. 2023Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Datacited by 1
  35. 2024ENSTRECT: A Stage-based Approach to 2.5D Structural Damage Detectioncited by 1
  36. 2024Memoryless Multimodal Anomaly Detection via Student-Teacher Network and Signed Distance Learningcited by 1
  37. 2025Unsupervised Anomaly Detection on Implicit Shape representations for Sarcopenia Detectioncited by 1
  38. 2025RS-OOD: A Vision-Language Augmented Framework for Out-of-Distribution Detection in Remote Sensingcited by 1

50 most cited of 66 All 66 (JSON)