The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Pose tracking from natural features on mobile phones
Abstract
In this paper we present two techniques for natural feature tracking in real-time on mobile phones. We achieve interactive frame rates of up to 20 Hz for natural feature tracking from textured planar targets on current-generation phones. We use an approach based on heavily modified state-of-the-art feature descriptors, namely SIFT and Ferns. While SIFT is known to be a strong, but computationally expensive feature descriptor, Ferns classification is fast, but requires large amounts of memory. This renders both original designs unsuitable for mobile phones. We give detailed descriptions on how we modified both approaches to make them suitable for mobile phones. We present evaluations on robustness and performance on various devices and finally discuss their appropriateness for augmented reality applications.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955
A performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.188 · Cited by 6,723
Machine Learning for High-Speed Corner Detection
Rosten, E., & Drummond, T. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_34 · Cited by 4,555
Self-citationParallel Tracking and Mapping for Small AR Workspaces
Klein, G., & Murray, D. (2007) · 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2007.4538852 · Cited by 4,346
Cited by
AprilTag: A robust and flexible visual fiducial system
Olson, E. (2011) · 2011 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5979561 · Cited by 1,770
Pose estimation for augmented reality: a hands-on survey
Marchand, E., Uchiyama, H., & Spindler, F. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2513408 · Cited by 746
Segmentation-Driven 6D Object Pose Estimation
Hu, Y., Hugonot, J., Fua, P., & Salzmann, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00350 · Cited by 333
Symmetrical SURF and Its Applications to Vehicle Detection and Vehicle Make and Model Recognition
Hsieh, J.-W., Chen, L.-C., & Chen, D.-Y. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2013.2294646 · Cited by 200
Like bees around the hive: a comparative study of a mobile augmented reality map
Morrison, A., Oulasvirta, A., Peltonen, P., Lemmela, S., Jacucci, G., Reitmayr, G., Näsänen, J., & Juustila, A. (2009) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1518991 · Cited by 199
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Metadata
- Authors
- Wagner, D., Reitmayr, G., Mulloni, A., Drummond, T., & Schmalstieg, D.
- Venue
- 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality
- Publisher
- IEEE
- Published
- 2008-09
- Pages
- 125-134
- OpenAlex
- W2132079375
- Cited by
- 420
- References
- 5
- FWCI
- 99.09
- Type
- conference paper
- Language
- en
- Open access
- closed
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