Limitations of Metric Loss for the Estimation of Joint Translation and Rotation

Philippe Roman, Pascal Desbarats, Jean-Philippe Domenger, Axel Buendia

Abstract

Localizing objects is a key challenge for robotics, augmented reality and mixed reality applications. Images taken in the real world feature many objects with challenging factors such as occlusions, motion blur and changing lights. In manufacturing industry scenes, a large majority of objects are poorly textured or highly reflective. Moreover, they often present symmetries which makes the localization task even more complicated. PoseNet is a deep neural network based on GoogleNet that predicts camera poses in indoor room and outdoor streets. We propose to evaluate this method for the problem of industrial object pose estimation by training the network on the T-LESS dataset. We demonstrate with our experiments that PoseNet is able to predict translation and rotation separately with high accuracy. However, our experiments also prove that it is not able to learn translation and rotation jointly. Indeed, one of the two modalities is either not learned by the network, or forgotten during training when the other is being learned. This justifies the fact that future works will require other formulation of the loss as well as other architectures in order to solve the pose estimation general problem.

Download


Paper Citation


in Harvard Style

Roman P., Desbarats P., Domenger J. and Buendia A. (2019). Limitations of Metric Loss for the Estimation of Joint Translation and Rotation.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4, pages 590-597. DOI: 10.5220/0007525005900597


in Bibtex Style

@conference{visapp19,
author={Philippe Roman and Pascal Desbarats and Jean-Philippe Domenger and Axel Buendia},
title={Limitations of Metric Loss for the Estimation of Joint Translation and Rotation},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={590-597},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007525005900597},
isbn={978-989-758-354-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Limitations of Metric Loss for the Estimation of Joint Translation and Rotation
SN - 978-989-758-354-4
AU - Roman P.
AU - Desbarats P.
AU - Domenger J.
AU - Buendia A.
PY - 2019
SP - 590
EP - 597
DO - 10.5220/0007525005900597