mlsanityData to 7 Oct 2026

Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

arXiv 2408.04604 ↗First posted 8 Aug 2024cs.CVcited by 25

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Abstract (arXiv)

High-resolution point clouds~(HRPCD) anomaly detection~(AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level; ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation; iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network~(IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network~(IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection~(AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://github.com/M-3LAB/Group3AD.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 33 of its 43 references that are in the map. First posted on arXiv 8 Aug 2024.

Builds on

33 of 43 references.

References · 33

  1. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
  2. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  3. 2019How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddingscited by 770
  4. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  5. 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
  6. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
  7. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  8. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  9. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  10. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  11. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  12. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
21 more
  1. 2019Representation Degeneration Problem in Training Natural Language Generation Modelscited by 250
  2. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  3. 2022CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understandingcited by 198
  4. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
  5. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  6. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 145
  7. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 138
  8. 2021Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localizationcited by 116
  9. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 115
  10. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 92
  11. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 89
  12. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  13. 2023Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcorecited by 75
  14. 2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74
  15. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 74
  16. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  17. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  18. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  19. 2021SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representationscited by 55
  20. 2024SoftPatch: Unsupervised Anomaly Detection with Noisy Datacited by 55
  21. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 15; 2026: 9.

25 papers.

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  1. 2024PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detectioncited by 30
  2. 2025MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detectioncited by 23
  3. 2025Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defectscited by 15
  4. 2025Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detectioncited by 11
  5. 2025Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representationcited by 9
  6. 2025Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detectioncited by 7
  7. 2025A Survey on Foundation-Model-Based Industrial Defect Detectioncited by 5
  8. 2025CASL: Curvature-Augmented Self-supervised Learning for 3D Anomaly Detectioncited by 5
  9. 2025A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Featurescited by 5
  10. 2025Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detectioncited by 4
  11. 2025Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detectioncited by 4
  12. 2026GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learningcited by 4
13 more
  1. 2026A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detectioncited by 3
  2. 2026Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detectioncited by 3
  3. 20253DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clusteringcited by 2
  4. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  5. 2025IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domaincited by 1
  6. 2026Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detectioncited by 1
  7. 2025Mentor3AD: Feature Reconstruction-based 3D Anomaly Detection via Multi-modality Mentor Learningcited by 0
  8. 2025Multi-View Reconstruction with Global Context for 3D Anomaly Detectioncited by 0
  9. 2026Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defectscited by 0
  10. 2026Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detectioncited by 0
  11. 2026Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detectioncited by 0
  12. 2026PC2-AD: Point Cloud Upsampling to Safeguard 3D Anomaly Detection with Resolution-constrained Edge Devicescited by 0
  13. 2026Towards Generalizable 3D Anomaly Detection via Relational Inconsistency Modelingcited by 0