Real-Time 3D-Teleimmersion

  • Kostas Daniilidis
  • Jane Mulligan
  • Raymond McKendall
  • David Schmid
  • Gerda Kamberova
  • Ruzena Bajcsy
Chapter
Part of the NATO Science Series book series (ASHT, volume 84)

Abstract

In this paper we present the first implementation of a new medium for telecollaboration. The realized testbed consists of two tele-cubicles at two Internet nodes. At each telecubicle a stereo-rig is used to provide an accurate dense 3D-reconstruction of a person in action. The two real dynamic worlds are transmitted over the network and visualized stereoscopically. The full-3D information facilitates interaction with any virtual object, demonstrating in an optimal way the confluence of graphics, vision, and communication.

In particular, the remote communication and the dynamic nature of telecollaboration put the challenge of optimal representation for graphics and vision. We treat the issues of limited bandwidth, latency, and processing power with a tunable 3D-representation where the user can choose the trade-off between delay and 3D-resolution by tuning the spatial resolution, the size of the working volume, and the uncertainty of reconstruction. Due to the limited number of cameras and displays our system can not provide the user with a surround-immersive feeling. However, it is the first system that uses 3D-real-data that are reconstructed online at another site. The system has been implemented with low-cost off-the-shelf hardware and has been successfully demonstrated in a local area network.

Keywords

Augmented Reality Stereo Vision Stereo Match Epipolar Line Correlation Window 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    N. Ayache and C. Hansen. Rectification of images for binocular and trinocular stereovision. In Proc. of 9th International Conference on Pattern Recognition, volume 1, pages 11–16, 1988.Google Scholar
  2. [2]
    P. Belhumeur. A bayesian approach to binocular stereopsis. Intl. J. of Computer Vision, 19(3):237–260, 1996.CrossRefGoogle Scholar
  3. [3]
    E. Chen and L. Williams. View interpolation for image synthesis. In ACM SIGGRAPH, pages 279–288, 1993.Google Scholar
  4. [4]
    F. Devernay. Computing differential properties of 3-D shapes from stereoscopic images without 3-D models. Technical Report RR-2304, INRIA, Sophia Antipolis, 1994.Google Scholar
  5. [5]
    U. Dhond and J. Aggrawal. Structure from stereo: a review. IEEE Transactions on Systems, Man, and Cybernetics, 19(6):1489–1510, 1989.MathSciNetCrossRefGoogle Scholar
  6. [6]
    O. Faugeras. Three-dimensional Computer Vision. MIT Press, Cambridge, MA, 1993.Google Scholar
  7. [7]
    K. Konolige. Small vision system: Hardware and implementation. In Eighth International Symposium on Robotics Research, Hayama, Japan, pages 203–212, 1997.Google Scholar
  8. [8]
    K. Kutulakos and J. Vallino. Calibration-free augmented reality. IEEE Trans, on Visualization and Computer Graphics, 4(l):l–20, 1998.Google Scholar
  9. [9]
    M. Macedonia and S. Noll. Real-time 60hz distortion correction on a silicon graphics IG. IEEE Computer Graphics and Applications, 5:76–82, 1998.Google Scholar
  10. [10]
    L. Matthies. Stereo vision for planetary rovers: Stochastic modeling to near real-time implementation. Intl. J. of Computer Vision, 8:71–91, 1992.CrossRefGoogle Scholar
  11. [11]
    S. Maybank and O. Faugeras. A theory of self-calibration of a moving camera. Intl. J. of Computer Vision, 8(2):123—151, 1992.Google Scholar
  12. [12]
    H. Moravec. Robot rover visual navigation. Computer Science: Artificial Intelligence, pages 105–108, 1980/1981.Google Scholar
  13. [13]
    P. Narayanan, P. Ränder, and T. Kanade. Constructing virtual worlds using dense stereo. In Proc, Intl. Conf. Computer Vision ICCV98, pages 3–10, 1998.Google Scholar
  14. [14]
    M. Okutomi and T. Kanade. A multiple-baseline stereo. IEEE Trans, on Pattern Analysis and Machine Intelligence, 15(4):353–363, 1993.CrossRefGoogle Scholar
  15. [15]
    D. Scharstein and R. Szeliski. Stereo matching with non-linear diffusion. In Proc. Int. Conf. Computer Vision and Pattern Recognition, pages 343–350. IEEE Computer Society, 1996.Google Scholar
  16. [16]
    S. M. Seitz and C. R. Dyer. View morphing. In ACM SIGGRAPH, pages 21–30, 1996.Google Scholar
  17. [17]
    C. Tomasi and R. Manduchi. Stereo without search. In Proc. European Conf. Computer Vision, pages 452–465, Cambridge, UK, 1996.Google Scholar
  18. [18]
    R. Tsai. A versatile camera calibration technique for high accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Trans. Robotics and Automation, 3:323–344, 1987.CrossRefGoogle Scholar
  19. [19]
    J. Woodfill and B. Von Herzen. Real time stereo vision on the PARTS reconfigurable computer. In IEEE Workshop on FPGAs for Custom Computing Machines, pages 201–210, 1997.Google Scholar
  20. [20]
    A. Yuille and T. Poggio. A generalized ordering constraint for stereo correspondence. AI Lab Memo 777, MIT, 1984.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2000

Authors and Affiliations

  • Kostas Daniilidis
  • Jane Mulligan
  • Raymond McKendall
  • David Schmid
  • Gerda Kamberova
  • Ruzena Bajcsy

There are no affiliations available

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