Combining Local and Global Pose Estimation for Precise Tracking of Similar Objects

Niklas Gard, Anna Hilsmann, Peter Eisert, Peter Eisert

2022

Abstract

In this paper, we present a multi-object 6D detection and tracking pipeline for potentially similar and non-textured objects. The combination of a convolutional neural network for object classification and rough pose estimation with a local pose refinement and an automatic mismatch detection enables direct application in real-time AR scenarios. A new network architecture, trained solely with synthetic images, allows simultaneous pose estimation of multiple objects with reduced GPU memory consumption and enhanced performance. In addition, the pose estimates are further improved by a local edge-based refinement step that explicitly exploits known object geometry information. For continuous movements, the sole use of local refinement reduces pose mismatches due to geometric ambiguities or occlusions. We showcase the entire tracking pipeline and demonstrate the benefits of the combined approach. Experiments on a challenging set of non-textured similar objects demonstrate the enhanced quality compared to the baseline method. Finally, we illustrate how the system can be used in a real AR assistance application within the field of construction.

Paper Citation

in Harvard Style

Gard N., Hilsmann A. and Eisert P. (2022). Combining Local and Global Pose Estimation for Precise Tracking of Similar Objects. 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 745-756. DOI: 10.5220/0010882700003124

in Bibtex Style

@conference{visapp22,
author={Niklas Gard and Anna Hilsmann and Peter Eisert},
title={Combining Local and Global Pose Estimation for Precise Tracking of Similar Objects},
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={745-756},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010882700003124},
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 - Combining Local and Global Pose Estimation for Precise Tracking of Similar Objects
SN - 978-989-758-555-5
AU - Gard N.
AU - Hilsmann A.
AU - Eisert P.
PY - 2022
SP - 745
EP - 756
DO - 10.5220/0010882700003124
PB - SciTePress