Linear N-Point Camera Pose Determination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Marker Tracking and HMD Calibration for a Video-Based Augmented Reality Conferencing System
IWAR '99 Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Fusing Points and Lines for High Performance Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
TimeWarp: interactive time travel with a mobile mixed reality game
Proceedings of the 10th international conference on Human computer interaction with mobile devices and services
Hybrid tracking algorithms for planar and non-planar structures subject to illumination changes
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
Going out: robust model-based tracking for outdoor augmented reality
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
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
Parallel Tracking and Mapping on a camera phone
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
Random model variation for universal feature tracking
Proceedings of the 18th ACM symposium on Virtual reality software and technology
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While tracking technologies based on fiducial markers have dominated the development of Augmented Reality (AR) applications for almost a decade, various real-time capable approaches to markerless tracking have recently been presented. However, most existing approaches do not yet achieve sufficient frame rates for AR on mobile phones or at least require an extensive training phase in advance. In this paper we will present our approach on feature based tracking applying robust SURF features. The implementation is more than one magnitude faster than previous ones, allowing running even on mobile phones at highly interactive rates. In contrast to other feature based approaches on mobile phones, our implementation may immediately track features captured from a photo without any training. Further, the approach is not restricted to planar surfaces, but may use features of 3D objects.