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Authors: Jorge Charco 1 ; Angel Sappa 2 ; Boris Vintimilla 3 and Henry Velesaca 3

Affiliations: 1 ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, ESPOL, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador, Universidad de Guayaquil, Delta and Kennedy Av., P.B. EC090514, Guayaquil, Ecuador ; 2 ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, ESPOL, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador, Computer Vision Center, Edifici O, Campus UAB, 08193 Bellaterra, Barcelona, Spain ; 3 ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, ESPOL, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador

ISBN: 978-989-758-402-2

ISSN: 2184-4321

Keyword(s): Relative Camera Pose Estimation, Siamese Architecture, Synthetic Data, Deep Learning, Multi-view Environments, Extrinsic Camera Parameters.

Abstract: This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The transfer learning consists of first training the network using pairs of images from the virtual-world scenario considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose estimation accuracy using the proposed model, as well as further improvements when the transfer learning strategy (synthetic-world data transfer learning real-world data) is considered to tackle the limitation on the training due to the reduced nu mber of pairs of real-images on most of the public data sets. (More)

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Paper citation in several formats:
Charco, J.; Sappa, A.; Vintimilla, B. and Velesaca, H. (2020). Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-402-2, ISSN 2184-4321, pages 498-505. DOI: 10.5220/0009167604980505

@conference{visapp20,
author={Jorge L. Charco. and Angel D. Sappa. and Boris X. Vintimilla. and Henry O. Velesaca.},
title={Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2020},
pages={498-505},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009167604980505},
isbn={978-989-758-402-2},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem
SN - 978-989-758-402-2
AU - Charco, J.
AU - Sappa, A.
AU - Vintimilla, B.
AU - Velesaca, H.
PY - 2020
SP - 498
EP - 505
DO - 10.5220/0009167604980505

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