# 3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised Anomaly

arXiv: https://arxiv.org/abs/2502.05761 · HTML: https://mlsanity.com/paper/2502.05761/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2025.

## Built on by (most cited first, 10 of 10)

- [OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning](https://mlsanity.com/paper/2505.22039/) (2025)
- [AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison](https://mlsanity.com/paper/2603.13779/) (2026)
- [Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines](https://mlsanity.com/paper/2606.07953/) (2026)
- [A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects](https://mlsanity.com/paper/2507.13378/) (2025)
- [ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categories](https://mlsanity.com/paper/2511.20169/) (2025)
- [MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models](https://mlsanity.com/paper/2604.10971/) (2026)
- [VD-DeepStack: Bridging Visual Comparison and Language Reasoning for Few-Shot Anomaly Detection](https://mlsanity.com/paper/2609.34949/) (2026)
- [PDD: Manifold-Prior Diverse Distillation for Medical Anomaly Detection](https://mlsanity.com/paper/2603.07142/) (2026)
- [UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting](https://mlsanity.com/paper/2604.02905/) (2026)
- [Large-Scale Universal Defect Generation: Foundation Models and Datasets](https://mlsanity.com/paper/2604.08915/) (2026)

## Abstract (arXiv)

Industrial anomaly detection achieves progress thanks to datasets such as MVTec-AD and VisA. However, they suffer from limitations in terms of the number of defect samples, types of defects, and availability of real-world scenes. These constraints inhibit researchers from further exploring the performance of industrial detection with higher accuracy. To this end, we propose a new large-scale anomaly detection dataset called 3CAD, which is derived from real 3C production lines. Specifically, the proposed 3CAD includes eight different types of manufactured parts, totaling 27,039 high-resolution images labeled with pixel-level anomalies. The key features of 3CAD are that it covers anomalous regions of different sizes, multiple anomaly types, and the possibility of multiple anomalous regions and multiple anomaly types per anomaly image. This is the largest and first anomaly detection dataset dedicated to 3C product quality control for community exploration and development. Meanwhile, we introduce a simple yet effective framework for unsupervised anomaly detection: a Coarse-to-Fine detection paradigm with Recovery Guidance (CFRG). To detect small defect anomalies, the proposed CFRG utilizes a coarse-to-fine detection paradigm. Specifically, we utilize a heterogeneous distillation model for coarse localization and then fine localization through a segmentation model. In addition, to better capture normal patterns, we introduce recovery features as guidance. Finally, we report the results of our CFRG framework and popular anomaly detection methods on the 3CAD dataset, demonstrating strong competitiveness and providing a highly challenging benchmark to promote the development of the anomaly detection field. Data and code are available: https://github.com/EnquanYang2022/3CAD.