mlsanityData to 7 Oct 2026

Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection

arXiv 2303.13194 ↗First posted 23 Mar 2023cs.CV cs.AIcited by 74

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Abstract (arXiv)

Point cloud (PCD) anomaly detection steadily emerges as a promising research area. This study aims to improve PCD anomaly detection performance by combining handcrafted PCD descriptions with powerful pre-trained 2D neural networks. To this end, this study proposes Complementary Pseudo Multimodal Feature (CPMF) that incorporates local geometrical information in 3D modality using handcrafted PCD descriptors and global semantic information in the generated pseudo 2D modality using pre-trained 2D neural networks. For global semantics extraction, CPMF projects the origin PCD into a pseudo 2D modality containing multi-view images. These images are delivered to pre-trained 2D neural networks for informative 2D modality feature extraction. The 3D and 2D modality features are aggregated to obtain the CPMF for PCD anomaly detection. Extensive experiments demonstrate the complementary capacity between 2D and 3D modality features and the effectiveness of CPMF, with 95.15% image-level AU-ROC and 92.93% pixel-level PRO on the MVTec3D benchmark. Code is available on https://github.com/caoyunkang/CPMF.

Key facts. Cited by 74 papers in the map (data through 7 Oct 2026). Builds on 25 of its 53 references that are in the map. First posted on arXiv 23 Mar 2023.

Builds on

25 of 53 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2016Wide Residual Networkscited by 3,057
  5. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  6. 2018Open3D: A Modern Library for 3D Data Processingcited by 717
  7. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 564
  8. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
  9. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  10. 2020PointContrast: Unsupervised Pre-training for 3D Point Cloud Understandingcited by 502
  11. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  12. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
13 more
  1. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 263
  2. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  3. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
  4. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 145
  5. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 138
  6. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 115
  7. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 92
  8. 2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74
  9. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  10. 2023Collaborative Discrepancy Optimization for Reliable Image Anomaly Localizationcited by 56
  11. 2017Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networkscited by 17
  12. 2021Unsupervised Landmark Detection Based Spatiotemporal Motion Estimation for 4D Dynamic Medical Imagescited by 9
  13. 2022Deep Open-Set Recognition for Silicon Wafer Production Monitoringcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 8; 2024: 15; 2025: 29; 2026: 22.

74 papers.

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  1. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  2. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  3. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  4. 2023Segment Any Anomaly without Training via Hybrid Prompt Regularizationcited by 59
  5. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
  6. 2024R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detectioncited by 45
  7. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  8. 2023Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Leadcited by 40
  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. 2024Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Propertiescited by 26
38 more
  1. 2024Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learningcited by 25
  2. 2024M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoisingcited by 24
  3. 2025MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detectioncited by 23
  4. 2024CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generationcited by 16
  5. 2025Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detectioncited by 16
  6. 2024Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Frameworkcited by 15
  7. 2025Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defectscited by 15
  8. 2024Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspectivecited by 12
  9. 2025Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detectioncited by 11
  10. 2025Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representationcited by 9
  11. 2024Towards Zero-shot 3D Anomaly Localizationcited by 8
  12. 2025Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detectioncited by 7
  13. 2025Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detectioncited by 6
  14. 2024Uni-3DAD: GAN-Inversion Aided Universal 3D Anomaly Detection on Model-free Productscited by 5
  15. 2025A Survey on Foundation-Model-Based Industrial Defect Detectioncited by 5
  16. 2025CASL: Curvature-Augmented Self-supervised Learning for 3D Anomaly Detectioncited by 5
  17. 2025A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Featurescited by 5
  18. 2025Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detectioncited by 4
  19. 2025Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detectioncited by 4
  20. 2025Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detectioncited by 4
  21. 2026GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learningcited by 4
  22. 2024Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Surveycited by 3
  23. 2025C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisorcited by 3
  24. 2025Enhancing Zero-Shot Anomaly Detection: CLIP-SAM Collaboration with Cascaded Promptscited by 3
  25. 2025IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detectioncited by 3
  26. 2026A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detectioncited by 3
  27. 2026Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detectioncited by 3
  28. 2025Out-of-Distribution Semantic Occupancy Predictioncited by 2
  29. 20253DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clusteringcited by 2
  30. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  31. 2025BridgeNet: A Unified Multimodal Framework for Bridging 2D and 3D Industrial Anomaly Detectioncited by 2
  32. 2025IAENet: An Importance-Aware Ensemble Model for 3D Point Cloud-Based Anomaly Detectioncited by 2
  33. 2025MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detectioncited by 2
  34. 2026Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detectioncited by 2
  35. 2023Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encodercited by 1
  36. 2023Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Datacited by 1
  37. 2024Memoryless Multimodal Anomaly Detection via Student-Teacher Network and Signed Distance Learningcited by 1
  38. 2025A 3D Multimodal Feature for Infrastructure Anomaly Detectioncited by 1

50 most cited of 74 All 74 (JSON)