mlsanityData to 14 Oct 2026

MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detection

arXiv 2509.10282 ↗First posted 12 Sep 2025cs.CV cs.LGcited by 2

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Abstract (arXiv)

Zero-shot 3D (ZS-3D) anomaly detection aims to identify defects in 3D objects without relying on labeled training data, making it especially valuable in scenarios constrained by data scarcity, privacy, or high annotation cost. However, most existing methods focus exclusively on point clouds, neglecting the rich semantic cues available from complementary modalities such as RGB images and texts priors. This paper introduces MCL-AD, a novel framework that leverages multimodal collaboration learning across point clouds, RGB images, and texts semantics to achieve superior zero-shot 3D anomaly detection. Specifically, we propose a Multimodal Prompt Learning Mechanism (MPLM) that enhances the intra-modal representation capability and inter-modal collaborative learning by introducing an object-agnostic decoupled text prompt and a multimodal contrastive loss. In addition, a collaborative modulation mechanism (CMM) is proposed to fully leverage the complementary representations of point clouds and RGB images by jointly modulating the RGB image-guided and point cloud-guided branches. Extensive experiments demonstrate that the proposed MCL-AD framework achieves state-of-the-art performance in ZS-3D anomaly detection.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 39 of its 46 references that are in the map. First posted on arXiv 12 Sep 2025.

Builds on

39 of 46 references.

References · 39

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2021Learning to Prompt for Vision-Language Modelscited by 2,281
  3. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  4. 2022MaPLe: Multi-modal Prompt Learningcited by 687
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  6. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  7. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
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  9. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 271
  10. 2022A Unified Model for Multi-class Anomaly Detectioncited by 268
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  12. 2022PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learningcited by 187
27 more
  1. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 178
  2. 2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Modelscited by 171
  3. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 139
  4. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  5. 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 130
  6. 2020Explainable Deep One-Class Classificationcited by 127
  7. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  8. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 93
  9. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 90
  10. 2023Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcorecited by 75
  11. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 75
  12. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  13. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  14. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  15. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  16. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 57
  17. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  18. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  19. 2023Multimodal Industrial Anomaly Detection by Crossmodal Feature Mappingcited by 42
  20. 2021Zero-Shot Learning on 3D Point Cloud Objects and Beyondcited by 38
  21. 2023Anomaly Detection under Distribution Shiftcited by 35
  22. 2023Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulationcited by 33
  23. 2024PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detectioncited by 29
  24. 2021Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Imagescited by 23
  25. 2023Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detectioncited by 21
  26. 2023When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysiscited by 21
  27. 2024Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Frameworkcited by 15

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detectioncited by 2
  2. 2026Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defectscited by 0