Advertisement

Robust fitting of 3D CAD models to video streams

  • Christophe Meilhac
  • Chahab Nastar
Session 7: Motion & Stereo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1310)

Abstract

We present a robust and accurate semi-automatic algorithm for registering and tracking a 3D geometric model in a 3D video stream. The algorithm is a generalization of the “Iterative Closest Point” technique. Each iteration is composed of two steps: computation of camera, parameters, and 3D/2D vertex matching. This last step is performed by polygon fitting in an edge image. To account for false matches, we use a robust M-estimation both for camera parameter estimation and 2D feature extraction. Experimental results show that accurate registration can be obtained even with very noisy outdoor images and incomplete data. Error analysis proves that. the accuracy is obtained at the pixel level.

Keywords

Video Stream Iterative Close Point Camera Parameter Iterative Close Point Matching Error 
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.
    P. J. Besl and N. D. McKay. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2):239–256, February 1992.Google Scholar
  2. 2.
    P.J. Huber. Robust Statistics. Wiley series in probability and mathematical statistics, 1981.Google Scholar
  3. 3.
    P. Jancéne, C. Meilhac, F. Neyret, X. Provot, J.-P. Tarel, J.-M. Vezien, and A. Verroust. RES: computing the interactions between real and virtual objects in video sequences. In the second IEEE workshop on networked realities, Boston Mass. (USA), October 26–28 1995.Google Scholar
  4. 4.
    H. Kollnig and H.H. Nagel. 3D pose estimation by fitting image gradients directly to polyhedral models. In International Conference on Computer Vision, pages 569–574, 1995.Google Scholar
  5. 5.
    R.Kumar and A. R. Hanson. Robust methods for estimating pose and sensitivity analysis. Computer Vision, Graphics, and Image Processing: Image, Understanding, 60(3):313–343, November 1994.Google Scholar
  6. 6.
    S. Lavallée and R. Szelisky. Recovering the position and orientation of free-form objects from image contours using 3D distance maps. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(4):378–390, April 1995.Google Scholar
  7. 7.
    R.K. Lenz and R.Y. Tsai. Techniques for calibration of the scale factor and image center for high accuracy 3-D machine vision metrology. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(5):713–720, 1988.Google Scholar
  8. 8.
    D.G. Lowe. Fitting parameterized three-dimensional models to images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(5):441–450, May 1991.Google Scholar
  9. 9.
    A.D. Worrall, G.D. Sullivan, and K.D. Baker. Pose Refinement of Active Models Using Forces in 3D. In European Conference on Computer Vision, pages A:341–350, 1994.Google Scholar
  10. 10.
    P. Wunsh and G. Hirzinger. Registration of CAD-Models to Images by Iterative Inverse Perspective Matching. In International Conference on Pattern Recognition, 1996.Google Scholar
  11. 11.
    Z. Zhang. Iterative point matching for registration of free-form curves and surfaces. International Journal of Computer Vision, 13(2):119–152, 1994.Google Scholar
  12. 12.
    Z. Zhang. Parameter estimation techniques: A tutorial with application to conic fitting. Technical Report 2676, INRIA, October 1995.Google Scholar
  13. 13.
    Z. Zhang. Determining the epipolar geometry and its uncertainty: A review. International Journal of Computer Vision, 1997.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Christophe Meilhac
    • 1
  • Chahab Nastar
    • 1
  1. 1.INRIA B.P. 105Le Chesnay CedexFrance

Personalised recommendations