Implementation of an Open Source Based Augmented Reality Engine for Cloud Authoring Frameworks

  • Nahuel Mangiarua DIIT, National University of La Matanza, San Justo, Buenos Aires, Argentina https://orcid.org/0000-0003-2674-7324
  • Jorge Ierache DIIT, National University of La Matanza, San Justo, Buenos Aires, Argentina
  • Marı́a José Abasolo Faculty of Informatics, National University of La Plata, La Plata, Buenos Aires, Argentina
Keywords: Augmented Realidy, Cloud Authoring Framework

Abstract

In recent years the technology around Augmented Reality has grown considerably, in particular on cloud based. In this paper we present a pipeline model and sample implementation that shows how an Augmented Reality engine can be built by leveraging advances in open source algorithm implementations. We also show how such an engine can be effectively integrated with cloud authoring tools to take advantage of the network connectivity and its computing power.

Downloads

Download data is not yet available.

References

C. Manresa Yee, M. J. Abásolo, R. Mas Sansó, and M. Vénere, ”Realidad virtual y realidad aumentada. Interfaces avanzadas”, 2011.

R. T. Azuma. ”A survey of augmented reality”, Presence: Teleoperators and Virtual Environments, 6(4):355–385, 1997.

C. Boonstra, R. V. D. Klein, and M. Lens- Fitzgerald. ”The Augmented Reality Hype Cycle”. Available at: https://huguesrey.wordpress.com-/2009/09/08/the-augmented-reality-hype-cycle- sprxmobile-mobile-service-architects/. Accessed on 2019-9-1.

Artoolkit. Available at: http://www.hitl.washington.edu/artoolkit/. Accessed on 2019-9-1.

Vuforia. Available at: https://developer.vuforia.com/. Accessed on 2019-9-1.

Layar. Available at: https://www.layar.com/. Accessed on 2019-9-1.

Google ARCore. Available at: https://developers.google.com/ar/. Accessed on 2019-9-1.

ARKit. Available at: https://developer.apple.com/arkit/. Accessed on 2019-9-1.

Aurasma. Available at: https://www.aurasma.com. Accessed on 2019-9-1.

Augment. Available at: https://www.augment.com/. Accessed on 2019-9-1.

Aumentary. Available at: http://www.aumentaty.com/index.php. Accessed on 2019-9-1.

Zappar. Available at: https://www.zappar.com/. Accessed on 2019-9-1.

J. Ierache, N. Mangiarua, N. Verdicchio, M. Becerra, N. Duarte, S. Igarza, ”Sistema de Catálogo para la Asistencia a la Creación, Publicación, Gestión y Explotación de Contenidos Multimediay Aplicaciones de Realidad Aumentada”. XVIII Argentine Congress of Computer Science, 2014.

J. Ierache, N. Mangiarua, S. Bevacqua, N. Verdicchio, M. Becerra, D. Sanz, M. Sena, F. Ortiz, N. Duarte, S. Igarza, ”Development of a Catalogs System for Augmented Reality Applications”. World Academy of Science, Engineering and Technology, International Science Index 97, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 9(1), 1-7, 2015.

OpenCV. Available at: www.opencv.org

C. Montalvo, F. Petrolo, D. Sanz, N. Mangiarua, N. Verdicchio, S. Igarza, J. Ierache, ”Knowledge Based Augmented Card System for Medical Assistance Over Mobile Devices”. Selected Paper, XXI Argentine Congress of Computer Science, pages 257-265, La Plata, Argentina, 2017.

N. Mangiarua, J. Ierache, M. Becerra, H. Maurice, S. Igarza, O. Spositto, ”Templates Framework for the Augmented Catalog System”. XXIV Argentine Congress of Computer Science, Tandil, Argentina, Pages 267-276, Revised Selected Papers, Springer Nature Switzerland, Springer, Computer Series Online, 2018.

S. A. K. Tareen and Z. Saleem, ”A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORBand BRISK”. International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, 2018, pp. 1-10, 2018.

H. Bay, A. Ess, T. Tuytelaars, and L.V. Gool, ”Speeded-up robust features (surf)”. Comput. Vis. Image Underst., 110(3):346–359, 2008.

E. Rosten and T. Drummond, ”Machine learning for high-speed corner detection”. In Proceedings of the 9th European Conference on Computer Vision - Volume Part I, ECCV’06, pages 430–443, Berlin, Heidelberg, 2006.

S. Leutenegger, M. Chli, and R. Y. Siegwart,”Brisk: Binary robust invariant scalable key-points”. In Proceedings of the 2011 International Conference on Computer Vision, ICCV ’11, pages 2548– 2555, Washington, DC, USA, 2011.

M. Agrawal, K. Konolige, and M. Rufus Blas, ”Censure: Center surround extremas for real-time feature detection and matching”. In David A. Forsyth, Philip H. S. Torr, and Andrew Zisserman, editors, ECCV (4), volume 5305 of Lecture Notes in Computer Science, pages 102–115. Springer, 2008.

E. Rublee, V. Rabaud, K. Konolige, and G. Brad-

ski, ”Orb: An efficient alternative to sift or surf”.In Proceedings of the 2011 International Conference on Computer Vision, ICCV ’11, pages 2564– 2571, Washington, DC, USA, 2011.

P. Vandergheynst, R. Ortiz, and A. Alahi, ”Freak:Fast retina keypoint”. 2013 IEEE Conferenceon Computer Vision and Pattern Recognition, 0:510–517, 2012.

E. Karami, S. Prasad, M. S. Shehata, ”Image matching using sift, surf, BRIEF and ORB: performance comparison for distorted images”. CoRRabs/1710.02726, 2017.

M. Muja and D. G. Lowe, ”Fast approximate nearest neighbors with automatic algorithm configuration”. In International Conference on Computer Vision Theory and Application, VISSAPP’09, pages 331–340. INSTICC Press, 2009.

B. D. Lucas and T. Kanade, ”An iterative image registration technique with an application to stereo vision”. In Proceedings of the 7th International Joint Conference on Artificial Intelligence, Volume 2, IJ- CAI’81, pages 674-679, San Francisco, CA, USA, 1981.

Published
2019-10-10
How to Cite
Mangiarua, N., Ierache, J., & Abasolo, M. J. (2019). Implementation of an Open Source Based Augmented Reality Engine for Cloud Authoring Frameworks. Journal of Computer Science and Technology, 19(2), e16. https://doi.org/10.24215/16666038.19.e16
Section
Original Articles