The MVTec 3D Anomaly Detection Dataset (MVTec 3D-AD)
MVTec 3D Anomaly Detection Dataset (MVTec 3D-AD) is a comprehensive 3D dataset for the task of unsupervised anomaly detection and localization. It contains over 4000 high-resolution scans acquired by an industrial 3D sensor. Each of the 10 different object categories comprises a set of defect-free training and validation samples and a test set of samples with various kinds of defects. Precise ground-truth annotations are provided for each anomalous test sample.
More information can be found in our corresponding paper titled "The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization". This work was awarded the Best Industrial Paper Award at the 17th International Conference on Computer Vision Theory and Applications (VISAPP / VISIGRAPP).
In order to ensure a fair and consistent comparison of new and existing methods on our dataset, we provide python scripts that allow an easy evaluation. For details on how to use the script, please have a look at the included readme file.
PLEASE NOTE: LICENSE TERMS & ATTRIBUTION
The data is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). In particular, it is not allowed to use the dataset for commercial purposes. If you are unsure whether or not your application violates the non-commercial use clause of the license, please contact us via the form below.
If you use this dataset in scientific work, please cite our paper:
Paul Bergmann, Xin Jin, David Sattlegger, Carsten Steger: The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization; in: Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, 202-213, 2022, DOI: 10.5220/0010865000003124.