NEMA: 6-DoF Pose Estimation Dataset for Deep Learning

Philippe Pérez de San Roman, Philippe Pérez de San Roman, Pascal Desbarats, Jean-Philippe Domenger, Axel Buendia, Axel Buendia

2022

Abstract

Maintenance is inevitable, time-consuming, expensive, and risky to production and maintenance operators. Porting maintenance support applications to mixed reality (MR) headsets would ease operations. To function, the application needs to anchor 3D graphics onto real objects, i.e. locate and track real-world objects in three dimensions. This task is known in the computer vision community as Six Degree of Freedom Pose Estimation (6-Dof) and is best solved using Convolutional Neural Networks (CNNs). Training them required numerous examples, but acquiring real labeled images for 6-DoF pose estimation is a challenge on its own. In this article, we propose first a thorough review of existing non-synthetic datasets for 6-DoF pose estimations. This allows identifying several reasons why synthetic training data has been favored over real training data. Nothing can replace real images. We show next that it is possible to overcome the limitations faced by previous datasets by presenting a new methodology for labeled images acquisition. And finally, we present a new dataset named NEMA that allows deep learning methods to be trained without the need for synthetic data.

Paper Citation

in Harvard Style

Pérez de San Roman P., Desbarats P., Domenger J. and Buendia A. (2022). NEMA: 6-DoF Pose Estimation Dataset for Deep Learning. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 682-690. DOI: 10.5220/0010913200003124

in Bibtex Style

@conference{visapp22,
author={Philippe Pérez de San Roman and Pascal Desbarats and Jean-Philippe Domenger and Axel Buendia},
title={NEMA: 6-DoF Pose Estimation Dataset for Deep Learning},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={682-690},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010913200003124},
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 4: VISAPP
TI - NEMA: 6-DoF Pose Estimation Dataset for Deep Learning
SN - 978-989-758-555-5
AU - Pérez de San Roman P.
AU - Desbarats P.
AU - Domenger J.
AU - Buendia A.
PY - 2022
SP - 682
EP - 690
DO - 10.5220/0010913200003124
PB - SciTePress