The Community for Technology Leaders
RSS Icon
Issue No.06 - November/December (2002 vol.22)
pp: 46-53
Gilles Simon , University of Henri Poincar?
Marie-Odile Berger , Institut National de Recherche en Informatique et en Automatique
<p>Tracking, or camera pose determination, is the main technical challenge in creating augmented reality. This article describes an efficient and reliable method for real-time camera tracking. This method is based on the knowledge of a piecewise planar structure in the scene and requires neither markers nor sensors to recover the viewpoint. The system yields results comparable in accuracy with full structure-and-motion methods but with better reliability. Results are presented, demonstrating tracking for indoors and outdoor urban scenes.</p>
pose estimation, real time camera tracking, planar structures, augmented reality
Gilles Simon, Marie-Odile Berger, "Pose Estimation for Planar Structures", IEEE Computer Graphics and Applications, vol.22, no. 6, pp. 46-53, November/December 2002, doi:10.1109/MCG.2002.1046628
1. J. Vallino, Interactive Augmented Reality, PhD thesis, Univ. of Rochester, New York, Dec. 1998.
2. R. Azuma et al., "Recent Advances in Augmented Reality," IEEE Computer Graphics and Applications, vol. 21, no. 6, Nov./Dec. 2001, pp. 34-47.
3. J.P. Mellor, "Realtime Camera Calibration for Enhanced Reality Visualization," Proc. Computer Vision, Virtual Reality, and Robotics in Medicine (CVRMed 95), Springer Verlag, Berlin, 1995, pp. 471-475.
4. U. Neumann and Y. Cho, "A Selftracking Augmented Reality System," Proc. ACM Symp. Virtual Reality Software and Technology, ACM Press, New York, 1996, pp. 109-115.
5. G. Simon, A.W. Fitzgibbon, and A. Zisserman, "Markerless Tracking Using Planar Structures in the Scene," Proc. Int'l Symp. Augmented Reality 2000 (ISAR 00), IEEE CS Press, Los Alamitos, Calif., 2000, pp. 120-128.
6. R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision. Cambridge Univ. Press, 2000.
7. M. Trajkovic and M. Hedley, "Fast Corner Detection," Image and Vision Computing, no. 16, 1998, pp. 75-87,
8. Z. Zhang, R. Deriche, O. Faugeras, and Q.T. Luong, “A Rubust Technique for Matching Two Uncalibrated Images through the Recovery of the Unknown Epipolar Geometry,” Artificial Intelligence J., vol. 78, pp. 87-119, 1995.
9. M.A. Fischler and R.C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Graphics and Image Processing, vol. 24, no. 6, pp. 381–395, June 1981.
10. O.D. Faugeras and G. Toscani, "The Calibration Problem for Stereo," Proc. IEEE Conf. Computer Vision and Pattern Recognition, IEEE CS Press, Los Alamitos, Calif., 1986, pp. 15-20.
11. D. Liebowitz, A. Criminisi, and A. Zisserman, "Creating Architectural Models from Images," Proc. Eurographics, Blackwell Publishers, Oxford, UK, 1999, pp. 39-50.
12. A.W. Fitzgibbon and A. Zisserman, “Automatic Camera Recovery for Closed or Open Image Sequences,” Proc. European Conf. Computer Vision, pp. 310–326, 1998.
54 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool