Authors:
Jorge L. Charco
1
;
2
;
Angel D. Sappa
3
;
1
;
Boris X. Vintimilla
1
and
Henry O. Velesaca
1
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
;
2
Universidad de Guayaquil, Delta and Kennedy Av., P.B. EC090514, Guayaquil, Ecuador
;
3
Computer Vision Center, Edifici O, Campus UAB, 08193 Bellaterra, Barcelona, Spain
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 n
umber of pairs of real-images on most of the public data sets.
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