DeNos22: A Pipeline to Learn Object Tracking Using Simulated Depth

Dominik Penk, Maik Horn, Christoph Strohmeyer, Frank Bauer, Marc Stamminger

2023

Abstract

We propose a novel pipeline to construct a learning based 6D object pose tracker, which is solely trained on synthetic depth images. The only required input is a (geometric) CAD model of the target object. Training data is synthesized by rendering stereo images of the CAD model, in front of a large variety of backgrounds generated by point-based re-renderings of prerecorded background scenes. Finally, depth from stereo is applied in order to mimic the behavior of depth sensors. The synthesized training input generalizes well to real-world scenes, but we further show how to improve real-world inference using robust estimators to counteract the errors introduced by the sim-to-real transfer. As a result, we show that our 6D pose trackers achieve state-of-the-art results without any annotated real-world data, solely based on a CAD-model of the target object.

Paper Citation

in Harvard Style

Penk D., Horn M., Strohmeyer C., Bauer F. and Stamminger M. (2023). DeNos22: A Pipeline to Learn Object Tracking Using Simulated Depth. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 953-962. DOI: 10.5220/0011635100003417

in Bibtex Style

@conference{visapp23,
author={Dominik Penk and Maik Horn and Christoph Strohmeyer and Frank Bauer and Marc Stamminger},
title={DeNos22: A Pipeline to Learn Object Tracking Using Simulated Depth},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={953-962},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011635100003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - DeNos22: A Pipeline to Learn Object Tracking Using Simulated Depth
SN - 978-989-758-634-7
AU - Penk D.
AU - Horn M.
AU - Strohmeyer C.
AU - Bauer F.
AU - Stamminger M.
PY - 2023
SP - 953
EP - 962
DO - 10.5220/0011635100003417
PB - SciTePress