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Authors: Paul Bergmann 1 ; 2 ; Xin Jin 3 ; David Sattlegger 1 and Carsten Steger 1

Affiliations: 1 MVTec Software GmbH, Germany ; 2 Technical University of Munich, Germany ; 3 Karlsruhe Institute of Technology, Germany

Keyword(s): Anomaly Detection, Dataset, Unsupervised Learning, Visual Inspection, 3D Computer Vision.

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 indica tes a considerable room for improvement. (More)

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Paper citation in several formats:
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; ISSN 2184-4321, SciTePress, pages 202-213. DOI: 10.5220/0010865000003124

@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},
issn={2184-4321},
}

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
IS - 2184-4321
AU - Bergmann, P.
AU - Jin, X.
AU - Sattlegger, D.
AU - Steger, C.
PY - 2022
SP - 202
EP - 213
DO - 10.5220/0010865000003124
PB - SciTePress

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