mlsanityData to 7 Oct 2026

Multimodal Industrial Anomaly Detection via Hybrid Fusion

arXiv 2303.00601 ↗First posted 1 Mar 2023cs.CVcited by 138

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Abstract (arXiv)

2D-based Industrial Anomaly Detection has been widely discussed, however, multimodal industrial anomaly detection based on 3D point clouds and RGB images still has many untouched fields. Existing multimodal industrial anomaly detection methods directly concatenate the multimodal features, which leads to a strong disturbance between features and harms the detection performance. In this paper, we propose Multi-3D-Memory (M3DM), a novel multimodal anomaly detection method with hybrid fusion scheme: firstly, we design an unsupervised feature fusion with patch-wise contrastive learning to encourage the interaction of different modal features; secondly, we use a decision layer fusion with multiple memory banks to avoid loss of information and additional novelty classifiers to make the final decision. We further propose a point feature alignment operation to better align the point cloud and RGB features. Extensive experiments show that our multimodal industrial anomaly detection model outperforms the state-of-the-art (SOTA) methods on both detection and segmentation precision on MVTec-3D AD dataset. Code is available at https://github.com/nomewang/M3DM.

Key facts. Cited by 138 papers in the map (data through 7 Oct 2026). Builds on 25 of its 41 references that are in the map. First posted on arXiv 1 Mar 2023.

Builds on

25 of 41 references.

References · 25

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  3. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  4. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  5. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  6. 2020Point Transformercited by 1,462
  7. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  8. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 647
  9. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  10. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
  11. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  12. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 456
13 more
  1. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  2. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  3. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  4. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  5. 2019OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representationscited by 199
  6. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
  7. 2022ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functionscited by 153
  8. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 145
  9. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 115
  10. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  11. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 55
  12. 2022CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous Drivingcited by 16
  13. 2022Benchmarking Unsupervised Anomaly Detection and Localizationcited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 19; 2024: 31; 2025: 43; 2026: 45.

138 papers.

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  1. 2023APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot ADcited by 128
  2. 2023AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Modelcited by 110
  3. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  4. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 74
  5. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  6. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  7. 2023Segment Any Anomaly without Training via Hybrid Prompt Regularizationcited by 59
  8. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  9. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
  10. 2024R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detectioncited by 45
  11. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  12. 2023Multimodal Industrial Anomaly Detection by Crossmodal Feature Mappingcited by 41
38 more
  1. 2023Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Leadcited by 40
  2. 2023Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulationcited by 33
  3. 2023DiAD: A Diffusion-based Framework for Multi-class Anomaly Detectioncited by 33
  4. 2024Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generationcited by 31
  5. 2024PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detectioncited by 30
  6. 2024Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detectioncited by 30
  7. 2024PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detectioncited by 29
  8. 2024Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Propertiescited by 26
  9. 2024SplatPose & Detect: Pose-Agnostic 3D Anomaly Detectioncited by 25
  10. 2024Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learningcited by 25
  11. 2024M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoisingcited by 24
  12. 2025MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detectioncited by 23
  13. 2023Component-aware anomaly detection framework for adjustable and logical industrial visual inspectioncited by 22
  14. 2023Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrievalcited by 22
  15. 2024AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusioncited by 21
  16. 2024Self-supervised Feature Adaptation for 3D Industrial Anomaly Detectioncited by 16
  17. 2024CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generationcited by 16
  18. 2025Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detectioncited by 16
  19. 2024A Survey on Safe Multi-Modal Learning Systemcited by 15
  20. 2024Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detectioncited by 15
  21. 2025Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defectscited by 15
  22. 2024Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspectivecited by 12
  23. 2025Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detectioncited by 12
  24. 2025Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detectioncited by 11
  25. 2026VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformercited by 10
  26. 2025Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detectioncited by 9
  27. 2025Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representationcited by 9
  28. 2024Towards Zero-shot 3D Anomaly Localizationcited by 8
  29. 2025Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detectioncited by 7
  30. 2023Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformerscited by 6
  31. 2024Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillationcited by 6
  32. 2025Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detectioncited by 6
  33. 2025Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memorycited by 6
  34. 2024PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Featurescited by 5
  35. 2024DMAD: Dual Memory Bank for Real-World Anomaly Detectioncited by 5
  36. 2024Uni-3DAD: GAN-Inversion Aided Universal 3D Anomaly Detection on Model-free Productscited by 5
  37. 2024RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observationscited by 5
  38. 2024DAS3D: Dual-modality Anomaly Synthesis for 3D Anomaly Detectioncited by 5

50 most cited of 138 All 138 (JSON)