mlsanityData to 14 Oct 2026

EasyNet: An Easy Network for 3D Industrial Anomaly Detection

arXiv 2307.13925 ↗First posted 26 Jul 2023cs.CVcited by 43

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Abstract (arXiv)

3D anomaly detection is an emerging and vital computer vision task in industrial manufacturing (IM). Recently many advanced algorithms have been published, but most of them cannot meet the needs of IM. There are several disadvantages: i) difficult to deploy on production lines since their algorithms heavily rely on large pre-trained models; ii) hugely increase storage overhead due to overuse of memory banks; iii) the inference speed cannot be achieved in real-time. To overcome these issues, we propose an easy and deployment-friendly network (called EasyNet) without using pre-trained models and memory banks: firstly, we design a multi-scale multi-modality feature encoder-decoder to accurately reconstruct the segmentation maps of anomalous regions and encourage the interaction between RGB images and depth images; secondly, we adopt a multi-modality anomaly segmentation network to achieve a precise anomaly map; thirdly, we propose an attention-based information entropy fusion module for feature fusion during inference, making it suitable for real-time deployment. Extensive experiments show that EasyNet achieves an anomaly detection AUROC of 92.6% without using pre-trained models and memory banks. In addition, EasyNet is faster than existing methods, with a high frame rate of 94.55 FPS on a Tesla V100 GPU.

Key facts. Cited by 43 papers in the map (data through 14 Oct 2026). Builds on 30 of its 38 references that are in the map. First posted on arXiv 26 Jul 2023.

Builds on

30 of 38 references.

References · 30

  1. 2017Squeeze-and-Excitation Networkscited by 4,789
  2. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  3. 2017Focal Loss for Dense Object Detectioncited by 752
  4. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  5. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
  6. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  7. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  8. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  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 321
  11. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 300
  12. 2022A Unified Model for Multi-class Anomaly Detectioncited by 268
18 more
  1. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  2. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 227
  3. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  4. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 178
  5. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 148
  6. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 146
  7. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 139
  8. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 117
  9. 2021Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localizationcited by 116
  10. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 93
  11. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 90
  12. 2022Anomalib: A Deep Learning Library for Anomaly Detectioncited by 75
  13. 2023Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcorecited by 75
  14. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 75
  15. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  16. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  17. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 57
  18. 2019Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detectioncited by 39

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 14; 2025: 15; 2026: 9.

43 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. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  5. 2024Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detectioncited by 36
  6. 2023DiAD: A Diffusion-based Framework for Multi-class Anomaly Detectioncited by 33
  7. 2024PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detectioncited by 29
  8. 2024Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learningcited by 25
  9. 2024M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoisingcited by 24
  10. 2024AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusioncited by 21
  11. 2024CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generationcited by 16
  12. 2024Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Frameworkcited by 15
31 more
  1. 2024Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspectivecited by 12
  2. 2025Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representationcited by 9
  3. 2024AnomalyControl: Learning Cross-modal Semantic Features for Controllable Anomaly Synthesiscited by 7
  4. 2024Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detectioncited by 6
  5. 2024Uni-3DAD: GAN-Inversion Aided Universal 3D Anomaly Detection on Model-free Productscited by 5
  6. 2024DAS3D: Dual-modality Anomaly Synthesis for 3D Anomaly Detectioncited by 5
  7. 2025AnomalyPainter: Vision-Language-Diffusion Synergy for Zero-Shot Realistic and Diverse Industrial Anomaly Synthesiscited by 5
  8. 2025Test-Time Distillation for Continual Model Adaptationcited by 5
  9. 2025Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmarkcited by 4
  10. 2026GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learningcited by 4
  11. 2023Multi-Scale Distillation for RGB-D Anomaly Detection on the PD-REAL Datasetcited by 3
  12. 2025IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detectioncited by 3
  13. 2025MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samplescited by 3
  14. 20253DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clusteringcited by 2
  15. 2025IAENet: An Importance-Aware Ensemble Model for 3D Point Cloud-Based Anomaly Detectioncited by 2
  16. 2025MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detectioncited by 2
  17. 2026Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detectioncited by 2
  18. 2025AnomalyHybrid: A Domain-agnostic Generative Framework for General Anomaly Detectioncited by 1
  19. 2025Resilient Multimodal Industrial Surface Defect Detection with Uncertain Sensors Availabilitycited by 1
  20. 2025PointAD+: Learning Hierarchical Representations for Zero-shot 3D Anomaly Detectioncited by 1
  21. 2026Multimodal Industrial Anomaly Detection via Geometric Priorcited by 1
  22. 2024Tuned Reverse Distillation: Enhancing Multimodal Industrial Anomaly Detection with Crossmodal Tunerscited by 0
  23. 2025Position: Untrained Machine Learning for Anomaly Detection by using 3D Point Cloud Datacited by 0
  24. 2025Domain Adaptation of Attention Heads for Zero-shot Anomaly Detectioncited by 0
  25. 2025The 3D Mirage: Probing and Taming 3D Hallucinationscited by 0
  26. 2026Text-Guided Multimodal Unified Industrial Anomaly Detectioncited by 0
  27. 2026Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detectioncited by 0
  28. 2026CMDS-AD: Cross-Modal Dual-Stream Decoupling for Few-Shot Anomaly Detectioncited by 0
  29. 2026CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detectioncited by 0
  30. 2026M2P-AD: Memory-to-Prototype Learning with Boundary-aware Score Refinement for 3D Anomaly Detectioncited by 0
  31. 2026MVFM-3DAD: Multi-view Flow Matching for 3D Anomaly Detection via Density Proxy Estimationcited by 0