CPU and RAM Performance Assessment for Different Marker Types in Augmented Reality Applications

Ciro Angeleri, Franco Marini, Damián Alvarez, Guillermo Leale, David Curras

Abstract

Augmented Reality is becoming a commonplace in the area of mobile application development industry and academia. This experiences on smartphones allowed a new world of experiences in the users daily life. Widely approaches such as fiducial markers or natural markers can be used to generate different scenarios and interactions. Two of the most important concerns are the limitations of resources in mobile devices and the consequent computational inefficiency. Thus, an important question to be raised in development teams is how the different parts that make up an AR experience can affect the performance of a mobile device and consequently the end user experience. Therefore, in this work we performed a quantitative assessment in terms of overall CPU and RAM usage when applying different marker types to mobile development. The results obtained are statistically significant and show that the use of markers with fewer number of vertices, such as a sphere performs better than others like a pyramid or a cube. With our results, we aim to provide a convenient means for technical leaders and development teams to reach an adequate decision when choosing a marker for generating new AR experiences.

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Paper Citation


in Harvard Style

Angeleri C., Marini F., Alvarez D., Leale G. and Curras D. (2021). CPU and RAM Performance Assessment for Different Marker Types in Augmented Reality Applications.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, ISBN 978-989-758-488-6, pages 271-277. DOI: 10.5220/0010302302710277


in Bibtex Style

@conference{grapp21,
author={Ciro Angeleri and Franco Marini and Damián Alvarez and Guillermo Leale and David Curras},
title={CPU and RAM Performance Assessment for Different Marker Types in Augmented Reality Applications},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP,},
year={2021},
pages={271-277},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010302302710277},
isbn={978-989-758-488-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP,
TI - CPU and RAM Performance Assessment for Different Marker Types in Augmented Reality Applications
SN - 978-989-758-488-6
AU - Angeleri C.
AU - Marini F.
AU - Alvarez D.
AU - Leale G.
AU - Curras D.
PY - 2021
SP - 271
EP - 277
DO - 10.5220/0010302302710277