Augmented Reality Camera Tracking with Homographies
IEEE Computer Graphics and Applications
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Monocular model-based 3D tracking of rigid objects
Foundations and Trends® in Computer Graphics and Vision
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Pose tracking from natural features on mobile phones
ISMAR '08 Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality
Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services
Shape recognition and pose estimation for mobile augmented reality
ISMAR '09 Proceedings of the 2009 8th IEEE 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
Utilizing sensor fusion in markerless mobile augmented reality
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
| Hi-index | 0.00 |
Whilst there has been a considerable progress in augmented reality (AR) over recent years, it has principally been related to either marker based or apriori mapped systems, which limits its opportunity for wide scale deployment. Recent advances in marker-less systems that have no apriori information, using techniques borrowed from robotic vision, are now finding their way into mobile augmented reality and are producing exciting results. However, unlike marker based and apriori tracking systems these techniques are independent of scale which is a vital component in ensuring that augmented objects are contextually sensitive to the environment they are projected upon. In this paper we address the problem of scale by adapting a Depth From Focus (DFF) technique, which has previously been limited to high-end cameras to a commercial mobile phone. The results clearly show that the technique is viable and adds considerably to the enhancement of mobile augmented reality. As the solution only requires an auto focusing camera, it is also applicable to other AR platforms.