The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization

Paul Bergmann, Paul Bergmann, Xin Jin, David Sattlegger, Carsten Steger

2022

Abstract

We introduce the first comprehensive 3D dataset for the task of unsupervised anomaly detection and localization. It is inspired by real-world visual inspection scenarios in which a model has to detect various types of defects on manufactured products, even if it is trained only on anomaly-free data. There are defects that manifest themselves as anomalies in the geometric structure of an object. These cause significant deviations in a 3D representation of the data. We employed a high-resolution industrial 3D sensor to acquire depth scans of 10 different object categories. For all object categories, we present a training and validation set, each of which solely consists of scans of anomaly-free samples. The corresponding test sets contain samples showing various defects such as scratches, dents, holes, contaminations, or deformations. Precise ground-truth annotations are provided for every anomalous test sample. An initial benchmark of 3D anomaly detection methods on our dataset indicates a considerable room for improvement.

Paper Citation

in Harvard Style

Bergmann P., Jin X., Sattlegger D. and Steger C. (2022). 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 (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 202-213. DOI: 10.5220/0010865000003124

in Bibtex Style

@conference{visapp22,
author={Paul Bergmann and Xin Jin and David Sattlegger and Carsten Steger},
title={The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={202-213},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010865000003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization
SN - 978-989-758-555-5
AU - Bergmann P.
AU - Jin X.
AU - Sattlegger D.
AU - Steger C.
PY - 2022
SP - 202
EP - 213
DO - 10.5220/0010865000003124
PB - SciTePress