Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Matching with PROSAC " Progressive Sample Consensus
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A survey of mobile and wireless technologies for augmented reality systems
Computer Animation and Virtual Worlds
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Multiple target detection and tracking with guaranteed framerates on mobile phones
ISMAR '09 Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality
Shape recognition and pose estimation for mobile augmented reality
ISMAR '09 Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality
Real-Time Detection and Tracking for Augmented Reality on Mobile Phones
IEEE Transactions on Visualization and Computer Graphics
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: interaction platforms and techniques
Scalable recognition and tracking for mobile augmented reality
Proceedings of the 9th ACM SIGGRAPH Conference on Virtual-Reality Continuum and its Applications in Industry
VR '10 Proceedings of the 2010 IEEE Virtual Reality Conference
Computers and Graphics
Smartphone as an augmented reality authoring tool via multi-touch based 3D interaction method
Proceedings of the 11th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry
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Advancements in mobile devices and vision technology have enabled mobile Augmented Reality (AR) to be serviced in real-time using natural features. However, in viewing AR while moving around in the real world, users often encounter new and diverse target objects. Whether the AR system is scalable to the number of target objects is a very crucial issue for mobile AR services in the real world. This scalability, however, has been severely limited because of the small internal storage capacity and memory of the mobile devices. In this paper, a new framework is proposed that achieves scalability for mobile AR. The scalability is achieved with a bag-of-visual-words based recognition module on the server side that is connected to the clients, which are mobile devices, through a conventional Wi-Fi network. On the client side, the coarse-to-fine tracking module enables robust tracking performance with natural features in real-time. In this study, we optimized modules in mobile devices for expediting pose-tracking processing and simultaneously enabled 3D rendering and animation in real-time. We also propose an efficient recognition method in which metadata are provided by the sensors of mobile devices. In the experiment, it takes approximately 0.2s for the cold start of an AR service initiated on a 10K object database with a recognition accuracy of 99.87%, which should be acceptable for a variety of real-world mobile AR applications.