A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photorealistic rendering for augmented reality using environment illumination
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Homography-based 2D Visual Tracking and Servoing
International Journal of Robotics Research
Towards anywhere augmentation
Image-Based Interactive Exploration of Real-World Environments
IEEE Computer Graphics and Applications
Improving the Agility of Keyframe-Based SLAM
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Technical Section: All around the map: Online spherical panorama construction
Computers and Graphics
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
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Vision-based global localization and mapping for mobile robots
IEEE Transactions on Robotics
Engineering animations in user interfaces
Proceedings of the 4th ACM SIGCHI symposium on Engineering interactive computing systems
iAR: an exploratory augmented reality system for mobile devices
Proceedings of the 18th ACM symposium on Virtual reality software and technology
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The acquisition of surround-view panoramas using a single hand-held or head-worn camera relies on robust real-time camera orientation tracking. In absence of robust tracking recovery methods, the complete acquisition process has to be re-started when tracking fails. This paper presents methodology for camera orientation relocalization, using virtual keyframes for online environment map construction. Instead of relying on real keyframes from incoming video, the proposed approach enables camera orientation relocalization by employing virtual keyframes which are distributed strategically within an environment map. We discuss our insights about a suitable number and distribution of virtual keyframes, as suggested by our experiments on virtual keyframe generation and orientation relocalization. After a shading correction step, we relocalize camera orientation in real-time by comparing the current camera frame to virtual keyframes. While expanding the captured environment map, we continue to simultaneously generate virtual keyframes within the completed portion of the map, as descriptors to estimate camera orientation. We implemented our camera orientation relocalizer with the help of a GPU fragment shader for real-time application, and evaluated the speed and accuracy of the proposed approach.