Advertisement

Keyframe Selection for Camera Motion and Structure Estimation from Multiple Views

  • Thorsten Thormählen
  • Hellward Broszio
  • Axel Weissenfeld
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3021)

Abstract

Estimation of camera motion and structure of rigid objects in the 3D world from multiple camera images by bundle adjustment is often performed by iterative minimization methods due to their low computational effort. These methods need a robust initialization in order to converge to the global minimum. In this paper a new criterion for keyframe selection is presented. While state of the art criteria just avoid degenerated camera motion configurations, the proposed criterion selects the keyframe pairing with the lowest expected estimation error of initial camera motion and object structure. The presented results show, that the convergence probability of bundle adjustment is significantly improved with the new criterion compared to the state of the art approaches.

Keywords

Feature Point Augmented Reality Camera Motion Camera Parameter Rigid Object 
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.

References

  1. 1.
    Triggs, B., McLauchlan, P., Hartley, R.I., Fitzgibbon, A.: Bundle adjustment – A modern synthesis. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) ICCV-WS 1999. LNCS, vol. 1883, p. 298. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  2. 2.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  3. 3.
    Pollefeys, M., Gool, L.V., Vergauwen, M., Cornelis, K., Verbiest, F., Tops, J.: Video-to-3d. In: Proceedings of Photogrammetric Computer Vision 2002 (ISPRS Commission III Symposium), International Archive of Photogrammetry and Remote Sensing, vol. 34, pp. 252–258 (2002)Google Scholar
  4. 4.
    Gibson, S., Cook, J., Howard, T., Hubbold, R., Oram, D.: Accurate camera calibration for off-line, video-based augmented reality. In: IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR 2002), Darmstadt, Germany (2002)Google Scholar
  5. 5.
    Fitzgibbon, A., Zisserman, A.: Automatic camera recovery for closed or open image sequences. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 311–326. Springer, Heidelberg (1998)Google Scholar
  6. 6.
    Georgescu, B., Meer, P.: Balanced recovery of 3d structure and camera motion from uncalibrated image sequences. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 294–308. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Torr, P., Fitzgibbon, A., Zisserman, A.: The problem of degeneracy in structure and motion recovery from uncalibrated images. International Journal of Computer Vision 32, 27–44 (1999)CrossRefGoogle Scholar
  8. 8.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: 4th Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  9. 9.
    Fischler, R.M.A., Bolles, C.: Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography. Communications of the ACM 24, 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Torr, P.H.S., Zisserman, A.: MLESAC: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding 78, 138–156 (2000)CrossRefGoogle Scholar
  11. 11.
    Tsai, R.Y.: A versatile camera calibration technique for high-accuracy 3-d machine vision metrology using off-the-shelf cameras and lenses. IEEE Transaction on Robotics and Automation 3, 323–344 (1987)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Thorsten Thormählen
    • 1
  • Hellward Broszio
    • 1
  • Axel Weissenfeld
    • 1
  1. 1.Information Technology LaboratoryUniversity of HannoverHannoverGermany

Personalised recommendations