# The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization

arXiv: https://arxiv.org/abs/2210.04570 · HTML: https://mlsanity.com/paper/2210.04570/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 10 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders](https://mlsanity.com/paper/1807.02011/) (2018)
- [FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows](https://mlsanity.com/paper/2111.07677/) (2021)
- [Adversarially Learned One-Class Classifier for Novelty Detection](https://mlsanity.com/paper/1802.09088/) (2018)
- [The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization](https://mlsanity.com/paper/2112.09045/) (2021)
- [BlenderProc](https://mlsanity.com/paper/1911.01911/) (2019)
- [Anomalib: A Deep Learning Library for Anomaly Detection](https://mlsanity.com/paper/2202.08341/) (2022)
- [Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection](https://mlsanity.com/paper/1909.11786/) (2019)
- [Deep Transfer Learning for Multiple Class Novelty Detection](https://mlsanity.com/paper/1903.02196/) (2019)
- [Cascaded V-Net using ROI masks for brain tumor segmentation](https://mlsanity.com/paper/1812.11588/) (2018)
- [Unsupervised 3D Brain Anomaly Detection](https://mlsanity.com/paper/2010.04717/) (2020)
- [Synthetic training data generation for deep learning based quality inspection](https://mlsanity.com/paper/2104.02980/) (2021)

## Built on by (most cited first, 30 of 57)

- [Deep Industrial Image Anomaly Detection: A Survey](https://mlsanity.com/paper/2301.11514/) (2023)
- [Multimodal Industrial Anomaly Detection via Hybrid Fusion](https://mlsanity.com/paper/2303.00601/) (2023)
- [Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection](https://mlsanity.com/paper/2403.12580/) (2024)
- [Real3D-AD: A Dataset of Point Cloud Anomaly Detection](https://mlsanity.com/paper/2309.13226/) (2023)
- [Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network](https://mlsanity.com/paper/2311.14897/) (2023)
- [IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing](https://mlsanity.com/paper/2301.13359/) (2023)
- [PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection](https://mlsanity.com/paper/2310.07716/) (2023)
- [EasyNet: An Easy Network for 3D Industrial Anomaly Detection](https://mlsanity.com/paper/2307.13925/) (2023)
- [Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping](https://mlsanity.com/paper/2312.04521/) (2023)
- [Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation](https://mlsanity.com/paper/2311.01117/) (2023)
- [The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2503.21622/) (2025)
- [PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection](https://mlsanity.com/paper/2410.00320/) (2024)
- [Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties](https://mlsanity.com/paper/2412.14592/) (2024)
- [M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising](https://mlsanity.com/paper/2406.02263/) (2024)
- [Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection](https://mlsanity.com/paper/2401.03145/) (2024)
- [Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection](https://mlsanity.com/paper/2504.14221/) (2025)
- [EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Models](https://mlsanity.com/paper/2503.14162/) (2025)
- [Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detection](https://mlsanity.com/paper/2503.03562/) (2025)
- [RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection](https://mlsanity.com/paper/2406.07176/) (2024)
- [A Survey on Industrial Anomaly Synthesis](https://mlsanity.com/paper/2502.16412/) (2025)
- [Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillation](https://mlsanity.com/paper/2405.13571/) (2024)
- [RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observations](https://mlsanity.com/paper/2410.00713/) (2024)
- [Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection](https://mlsanity.com/paper/2504.03306/) (2025)
- [SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark](https://mlsanity.com/paper/2506.21549/) (2025)
- [Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark](https://mlsanity.com/paper/2505.10996/) (2025)
- [2D_3D Feature Fusion via Cross-Modal Latent Synthesis and Attention Guided Restoration for Industrial Anomaly Detection](https://mlsanity.com/paper/2510.21793/) (2025)
- [GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning](https://mlsanity.com/paper/2602.19206/) (2026)
- [Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspective](https://mlsanity.com/paper/2603.02629/) (2026)
- [Multi-Scale Distillation for RGB-D Anomaly Detection on the PD-REAL Dataset](https://mlsanity.com/paper/2311.04095/) (2023)
- [Set Features for Anomaly Detection](https://mlsanity.com/paper/2311.14773/) (2023)

## 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.