mlsanityData to 7 Oct 2026

Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties

arXiv 2412.14592 ↗First posted 19 Dec 2024cs.CV cs.LGcited by 26

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Abstract (arXiv)

Object anomaly detection is essential for industrial quality inspection, yet traditional single-sensor methods face critical limitations. They fail to capture the wide range of anomaly types, as single sensors are often constrained to either external appearance, geometric structure, or internal properties. To overcome these challenges, we introduce MulSen-AD, the first high-resolution, multi-sensor anomaly detection dataset tailored for industrial applications. MulSen-AD unifies data from RGB cameras, laser scanners, and lock-in infrared thermography, effectively capturing external appearance, geometric deformations, and internal defects. The dataset spans 15 industrial products with diverse, real-world anomalies. We also present MulSen-AD Bench, a benchmark designed to evaluate multi-sensor methods, and propose MulSen-TripleAD, a decision-level fusion algorithm that integrates these three modalities for robust, unsupervised object anomaly detection. Our experiments demonstrate that multi-sensor fusion substantially outperforms single-sensor approaches, achieving 96.1% AUROC in object-level detection accuracy. These results highlight the importance of integrating multi-sensor data for comprehensive industrial anomaly detection.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 30 of its 43 references that are in the map. First posted on arXiv 19 Dec 2024.

Builds on

30 of 43 references.

References · 30

  1. 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,306
  2. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  3. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  4. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 464
  5. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 456
  6. 2019PointPainting: Sequential Fusion for 3D Object Detectioncited by 449
  7. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
  8. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  9. 2022DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detectioncited by 224
  10. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 222
  11. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
  12. 2021Multimodal Virtual Point 3D Detectioncited by 166
18 more
  1. 2020EPNet: Enhancing Point Features with Image Semantics for 3D Object Detectioncited by 164
  2. 2022DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detectioncited by 153
  3. 2020CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detectioncited by 147
  4. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  5. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 138
  6. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 138
  7. 2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
  8. 2022DeepInteraction: 3D Object Detection via Modality Interactioncited by 125
  9. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 92
  10. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  11. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 74
  12. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  13. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  14. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 55
  15. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
  16. 2024LogiCode: an LLM-Driven Framework for Logical Anomaly Detectioncited by 18
  17. 2024Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmarkcited by 15
  18. 2021Radar Voxel Fusion for 3D Object Detectioncited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 12; 2026: 14.

26 papers.

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  1. 2025Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defectscited by 15
  2. 2025Towards Cross-View Point Correspondence in Vision-Language Modelscited by 12
  3. 2025Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detectioncited by 6
  4. 2026Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspectivecited by 4
  5. 2025Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmarkcited by 3
  6. 2025C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisorcited by 3
  7. 2025IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detectioncited by 3
  8. 2026Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselinescited by 3
  9. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  10. 2025ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categoriescited by 2
  11. 2026Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detectioncited by 2
  12. 2025ASBench: Image Anomalies Synthesis Benchmark for Anomaly Detectioncited by 1
14 more
  1. 2026M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detectioncited by 1
  2. 2025ViSTR-GP: Online Cyberattack Detection via Vision-to-State Tensor Regression and Gaussian Processes in Automated Robotic Operationscited by 0
  3. 2025The 3D Mirage: Probing and Taming 3D Hallucinationscited by 0
  4. 2025Physic-HM: Restoring Physical Generative Logic in Multimodal Anomaly Detection via Hierarchical Modulationcited by 0
  5. 2026Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defectscited by 0
  6. 2026Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detectioncited by 0
  7. 2026ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detectioncited by 0
  8. 2026Breaking the Rigid Prior: Towards Articulated 3D Anomaly Detectioncited by 0
  9. 2026DifferAD-R1: A Difference-Guided IndustrialAnomaly Localization with Multimodal LargeLanguage Modelscited by 0
  10. 2026M2P-AD: Memory-to-Prototype Learning with Boundary-aware Score Refinement for 3D Anomaly Detectioncited by 0
  11. 2026Understanding and Overcoming Cross-modal Fusion Bias in Multimodal Anomaly Detection From A Fisher Information Perspectivecited by 0
  12. 2026LIBAD: A Multimodal Anomaly Detection Benchmark for Li-Ion Battery Electrode Manufacturingcited by 0
  13. 2026Multi-Modal Anomaly Detection: A Surveycited by 0
  14. 2026Beyond Geometry: Benchmarking and Consistency Reasoning for 3D Logical Anomaly Detectioncited by 0