Recent Advances in Augmented Reality
IEEE Computer Graphics and Applications
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
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
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
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
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Computer vision technology applied to MR-based pre-visualization in filmmaking
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
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This paper presents camera calibration and tracking method for mixed reality based pre-visualization system for filmmaking. The proposed calibration method collects environmental information required for tracking efficiently since the rough camera path and target environment are known before actual shooting. Previous camera tracking methods using natural feature are suitable for outdoor environment. However, it takes large human cost to construct the database. Our proposed method reduces the cost of calibration process by using fiducial markers. Fiducial markers are used as reference points and feature landmark database is constructed automatically. In shooting phase, moreover, the speed and robustness of tracking are improved by using SIFT descriptor.