# M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising

arXiv: https://arxiv.org/abs/2406.02263 · HTML: https://mlsanity.com/paper/2406.02263/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 65 of its 87 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 30 of 65)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [DivideMix: Learning with Noisy Labels as Semi-supervised Learning](https://mlsanity.com/paper/2002.07394/) (2020)
- [RegionCLIP: Region-based Language-Image Pretraining](https://mlsanity.com/paper/2112.09106/) (2021)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [Extract Free Dense Labels from CLIP](https://mlsanity.com/paper/2112.01071/) (2021)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting](https://mlsanity.com/paper/2112.01518/) (2021)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [PointCLIP: Point Cloud Understanding by CLIP](https://mlsanity.com/paper/2112.02413/) (2021)
- [Masked Autoencoders for Point Cloud Self-supervised Learning](https://mlsanity.com/paper/2203.06604/) (2022)
- [Measuring Robustness to Natural Distribution Shifts in Image Classification](https://mlsanity.com/paper/2007.00644/) (2020)
- [Anomaly Detection via Reverse Distillation from One-Class Embedding](https://mlsanity.com/paper/2201.10703/) (2022)
- [CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows](https://mlsanity.com/paper/2107.12571/) (2021)
- [End-to-End Semi-Supervised Object Detection with Soft Teacher](https://mlsanity.com/paper/2106.09018/) (2021)
- [WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation](https://mlsanity.com/paper/2303.14814/) (2023)
- [Unbiased Teacher for Semi-Supervised Object Detection](https://mlsanity.com/paper/2102.09480/) (2021)
- [Multiresolution Knowledge Distillation for Anomaly Detection](https://mlsanity.com/paper/2011.11108/) (2020)
- [ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding](https://mlsanity.com/paper/2212.05171/) (2022)
- [SimpleNet: A Simple Network for Image Anomaly Detection and Localization](https://mlsanity.com/paper/2303.15140/) (2023)
- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)

## Built on by (most cited first, 24 of 24)

- [MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection](https://mlsanity.com/paper/2410.09453/) (2024)
- [PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection](https://mlsanity.com/paper/2410.00320/) (2024)
- [Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspective](https://mlsanity.com/paper/2412.17297/) (2024)
- [Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detection](https://mlsanity.com/paper/2510.17611/) (2025)
- [A Survey on Foundation-Model-Based Industrial Defect Detection](https://mlsanity.com/paper/2502.19106/) (2025)
- [Towards Fine-Grained Vision-Language Alignment for Few-Shot Anomaly Detection](https://mlsanity.com/paper/2510.26464/) (2025)
- [Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models](https://mlsanity.com/paper/2505.13828/) (2025)
- [MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples](https://mlsanity.com/paper/2511.10047/) (2025)
- [Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detection](https://mlsanity.com/paper/2604.03972/) (2026)
- [A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects](https://mlsanity.com/paper/2507.13378/) (2025)
- [Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Era](https://mlsanity.com/paper/2511.00540/) (2025)
- [PointAD+: Learning Hierarchical Representations for Zero-shot 3D Anomaly Detection](https://mlsanity.com/paper/2509.03277/) (2025)
- [Multimodal Industrial Anomaly Detection via Geometric Prior](https://mlsanity.com/paper/2603.22757/) (2026)
- [Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detection](https://mlsanity.com/paper/2607.10544/) (2026)
- [Mentor3AD: Feature Reconstruction-based 3D Anomaly Detection via Multi-modality Mentor Learning](https://mlsanity.com/paper/2505.21420/) (2025)
- [MID: A Self-supervised Multimodal Iterative Denoising Framework](https://mlsanity.com/paper/2511.00997/) (2025)
- [Physic-HM: Restoring Physical Generative Logic in Multimodal Anomaly Detection via Hierarchical Modulation](https://mlsanity.com/paper/2512.21650/) (2025)
- [Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defects](https://mlsanity.com/paper/2604.01171/) (2026)
- [Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detection](https://mlsanity.com/paper/2604.04658/) (2026)
- [SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection](https://mlsanity.com/paper/2604.05632/) (2026)
- [Real-IAD MVN: A Multi-View Normal Vector Dataset and Benchmark for High-Fidelity Industrial Anomaly Detection](https://mlsanity.com/paper/2605.07149/) (2026)
- [MVFM-3DAD: Multi-view Flow Matching for 3D Anomaly Detection via Density Proxy Estimation](https://mlsanity.com/paper/2608.12148/) (2026)
- [Multi-Modal Anomaly Detection: A Survey](https://mlsanity.com/paper/2608.24937/) (2026)
- [When Point Clouds Outperform Pixels: Rethinking Zero-Shot Multimodal Anomaly Detection](https://mlsanity.com/paper/2609.25793/) (2026)

## Abstract (arXiv)

Existing industrial anomaly detection methods primarily concentrate on unsupervised learning with pristine RGB images. Yet, both RGB and 3D data are crucial for anomaly detection, and the datasets are seldom completely clean in practical scenarios. To address above challenges, this paper initially delves into the RGB-3D multi-modal noisy anomaly detection, proposing a novel noise-resistant M3DM-NR framework to leveraging strong multi-modal discriminative capabilities of CLIP. M3DM-NR consists of three stages: Stage-I introduces the Suspected References Selection module to filter a few normal samples from the training dataset, using the multimodal features extracted by the Initial Feature Extraction, and a Suspected Anomaly Map Computation module to generate a suspected anomaly map to focus on abnormal regions as reference. Stage-II uses the suspected anomaly maps of the reference samples as reference, and inputs image, point cloud, and text information to achieve denoising of the training samples through intra-modal comparison and multi-scale aggregation operations. Finally, Stage-III proposes the Point Feature Alignment, Unsupervised Feature Fusion, Noise Discriminative Coreset Selection, and Decision Layer Fusion modules to learn the pattern of the training dataset, enabling anomaly detection and segmentation while filtering out noise. Extensive experiments show that M3DM-NR outperforms state-of-the-art methods in 3D-RGB multi-modal noisy anomaly detection.