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Mentor3AD: Feature Reconstruction-based 3D Anomaly Detection via Multi-modality Mentor Learning

arXiv 2505.21420 ↗First posted 27 May 2025cs.CVcited by 0

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Abstract (arXiv)

Multimodal feature reconstruction is a promising approach for 3D anomaly detection, leveraging the complementary information from dual modalities. We further advance this paradigm by utilizing multi-modal mentor learning, which fuses intermediate features to further distinguish normal from feature differences. To address these challenges, we propose a novel method called Mentor3AD, which utilizes multi-modal mentor learning. By leveraging the shared features of different modalities, Mentor3AD can extract more effective features and guide feature reconstruction, ultimately improving detection performance. Specifically, Mentor3AD includes a Mentor of Fusion Module (MFM) that merges features extracted from RGB and 3D modalities to create a mentor feature. Additionally, we have designed a Mentor of Guidance Module (MGM) to facilitate cross-modal reconstruction, supported by the mentor feature. Lastly, we introduce a Voting Module (VM) to more accurately generate the final anomaly score. Extensive comparative and ablation studies on MVTec 3D-AD and Eyecandies have verified the effectiveness of the proposed method.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 35 of its 43 references that are in the map. First posted on arXiv 27 May 2025.

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35 of 43 references.

References · 35

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  10. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  11. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  12. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 178
23 more
  1. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 148
  2. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 139
  3. 2021Fully Convolutional Cross-Scale-Flows for Image-based Defect Detectioncited by 117
  4. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 117
  5. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 93
  6. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  7. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 75
  8. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  9. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 57
  10. 2024R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detectioncited by 45
  11. 2023Multimodal Industrial Anomaly Detection by Crossmodal Feature Mappingcited by 42
  12. 2024PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detectioncited by 30
  13. 2024Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detectioncited by 30
  14. 2024PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detectioncited by 29
  15. 2024SplatPose & Detect: Pose-Agnostic 3D Anomaly Detectioncited by 25
  16. 2024Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learningcited by 25
  17. 2024M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoisingcited by 24
  18. 2024CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generationcited by 16
  19. 2024Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Frameworkcited by 15
  20. 2024Looking 3D: Anomaly Detection with 2D-3D Alignmentcited by 13
  21. 2024SplatPose+: Real-time Image-Based Pose-Agnostic 3D Anomaly Detectioncited by 11
  22. 2025A Survey on Industrial Anomaly Synthesiscited by 7
  23. 2025Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detectioncited by 4

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