mlsanityData to 7 Oct 2026

The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization

arXiv 2210.04570 ↗First posted 10 Oct 2022cs.CVcited by 55

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Abstract (arXiv)

We present Eyecandies, a novel synthetic dataset for unsupervised anomaly detection and localization. Photo-realistic images of procedurally generated candies are rendered in a controlled environment under multiple lightning conditions, also providing depth and normal maps in an industrial conveyor scenario. We make available anomaly-free samples for model training and validation, while anomalous instances with precise ground-truth annotations are provided only in the test set. The dataset comprises ten classes of candies, each showing different challenges, such as complex textures, self-occlusions and specularities. Furthermore, we achieve large intra-class variation by randomly drawing key parameters of a procedural rendering pipeline, which enables the creation of an arbitrary number of instances with photo-realistic appearance. Likewise, anomalies are injected into the rendering graph and pixel-wise annotations are automatically generated, overcoming human-biases and possible inconsistencies. We believe this dataset may encourage the exploration of original approaches to solve the anomaly detection task, e.g. by combining color, depth and normal maps, as they are not provided by most of the existing datasets. Indeed, in order to demonstrate how exploiting additional information may actually lead to higher detection performance, we show the results obtained by training a deep convolutional autoencoder to reconstruct different combinations of inputs.

Key facts. Cited by 55 papers in the map (data through 7 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 10 Oct 2022.

Builds on

17 of 37 references.

References · 17

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  3. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  4. 2020TartanAir: A Dataset to Push the Limits of Visual SLAMcited by 494
  5. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  6. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  7. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 263
  8. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  9. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  10. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
  11. 2019BlenderProccited by 76
  12. 2022Anomalib: A Deep Learning Library for Anomaly Detectioncited by 75
5 more
  1. 2019Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detectioncited by 39
  2. 2019Deep Transfer Learning for Multiple Class Novelty Detectioncited by 33
  3. 2018Cascaded V-Net using ROI masks for brain tumor segmentationcited by 26
  4. 2020Unsupervised 3D Brain Anomaly Detectioncited by 12
  5. 2021Synthetic training data generation for deep learning based quality inspectioncited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 11; 2024: 11; 2025: 18; 2026: 15.

55 papers.

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  1. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  2. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 138
  3. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 138
  4. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  5. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  6. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  7. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
  8. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  9. 2023Multimodal Industrial Anomaly Detection by Crossmodal Feature Mappingcited by 41
  10. 2023Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulationcited by 33
  11. 2024PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detectioncited by 29
  12. 2025The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detectioncited by 28
38 more
  1. 2024Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Propertiescited by 26
  2. 2024M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoisingcited by 24
  3. 2024Self-supervised Feature Adaptation for 3D Industrial Anomaly Detectioncited by 16
  4. 2025Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detectioncited by 12
  5. 2025EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Modelscited by 11
  6. 2025Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detectioncited by 9
  7. 2024RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detectioncited by 8
  8. 2025A Survey on Industrial Anomaly Synthesiscited by 7
  9. 2024Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillationcited by 6
  10. 2024RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observationscited by 5
  11. 2025Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detectioncited by 5
  12. 2025SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmarkcited by 5
  13. 20252D_3D Feature Fusion via Cross-Modal Latent Synthesis and Attention Guided Restoration for Industrial Anomaly Detectioncited by 4
  14. 2026GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learningcited by 4
  15. 2026Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspectivecited by 4
  16. 2023Multi-Scale Distillation for RGB-D Anomaly Detection on the PD-REAL Datasetcited by 3
  17. 2023Set Features for Anomaly Detectioncited by 3
  18. 2025Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmarkcited by 3
  19. 2025IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detectioncited by 3
  20. 2025MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samplescited by 3
  21. 2024Attention Fusion Reverse Distillation for Multi-Lighting Image Anomaly Detectioncited by 2
  22. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  23. 2025IAENet: An Importance-Aware Ensemble Model for 3D Point Cloud-Based Anomaly Detectioncited by 2
  24. 2025MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detectioncited by 2
  25. 20253D-ADAM: A Dataset for 3D Anomaly Detection in Additive Manufacturingcited by 1
  26. 2025PointAD+: Learning Hierarchical Representations for Zero-shot 3D Anomaly Detectioncited by 1
  27. 2025From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challengecited by 1
  28. 2026Multimodal Industrial Anomaly Detection via Geometric Priorcited by 1
  29. 2024Learning to Be a Transformer to Pinpoint Anomaliescited by 0
  30. 2024Tuned Reverse Distillation: Enhancing Multimodal Industrial Anomaly Detection with Crossmodal Tunerscited by 0
  31. 2025Mentor3AD: Feature Reconstruction-based 3D Anomaly Detection via Multi-modality Mentor Learningcited by 0
  32. 2026Incremental dimension reduction for efficient and accurate visual anomaly detectioncited by 0
  33. 2026Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defectscited by 0
  34. 2026Modulate-and-Map: Crossmodal Feature Mapping with Cross-View Modulation for 3D Anomaly Detectioncited by 0
  35. 2026ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detectioncited by 0
  36. 2026Text-Guided Multimodal Unified Industrial Anomaly Detectioncited by 0
  37. 2026Align3D-AD: Cross-Modal Feature Alignment and Dual-Prompt Learning for Zero-shot 3D Anomaly Detectioncited by 0
  38. 2026Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detectioncited by 0

50 most cited of 55 All 55 (JSON)