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Surviving Dominant Planes in Uncalibrated Structure and Motion Recovery

  • Marc Pollefeys
  • Frank Verbiest
  • Luc Van Gool
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2351)

Abstract

In this paper we address the problem of uncalibrated structure and motion recovery from image sequences that contain dominant planes in some of the views. Traditional approaches fail when the features common to three consecutive views are all located on a plane. This happens because in the uncalibrated case there is a fundamental ambiguity in relating the structure before and after the plane. This is, however, a situation that is often hard to avoid in man-made environments. We propose a complete approach that detects the problem and defers the computation of parameters that are ambiguous in projective space (i.e. the registration between partial reconstructions only sharing a common plane and poses of cameras only seeing planar features) till after self-calibration. Also a new linear self-calibration algorithm is proposed that couples the intrinsics between multiple subsequences. The final result is a complete metric 3D reconstruction of both structure and motion for the whole sequence. Experimental results on real image sequences show that the approach yields very good results.

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References

  1. 1.
    A. Bartoli and P. Sturm, “Constrained Structure and Motion from N Views of a Piecewise Planar Scene”, VAA’01-In Proceedings of the International Symposium on Virtual and Augmented Architecture, Dublin, Ireland, pp. 195–206, June 2001.Google Scholar
  2. 2.
    P. Beardsley, P. Torr, and A. Zisserman. “3D model acquisition from extended image sequences”. In Proc. European Conf. on Computer Vision, LNCS 1064, Vol. 2, Springer-Verlag, pages 683–695, 1996.Google Scholar
  3. 3.
    O. Faugeras, “What can be seen in three dimensions with an uncalibrated stereo rig”, Computer Vision-ECCV’92, Lecture Notes in Computer Science, Vol. 588, Springer-Verlag, pp. 563–578, 1992.Google Scholar
  4. 4.
    O. Faugeras, Q.-T. Luong and S. Maybank. “Camera self-calibration: Theory and experiments”, Computer Vision-ECCV’92, Lecture Notes in Computer Science, Vol. 588, Springer-Verlag, pp. 321–334, 1992.Google Scholar
  5. 5.
    M. Fischler and R. Bolles, “RANdom SAmpling Consensus: a paradigm for model fitting with application to image analysis and automated cartography”, Commun. Assoc. Comp. Mach., 24:381–95, 1981.MathSciNetGoogle Scholar
  6. 6.
    A. Fitzgibbon and A. Zisserman, “Automatic camera recovery for closed or open image sequences”, Computer Vision-ECCV’98, vol. 1, Lecture Notes in Computer Science, Vol. 1406, Springer-Verlag, 1998. pp. 311–326, 1998.CrossRefGoogle Scholar
  7. 7.
    R. Haralick, C. Lee, K. Ottenberg, and M. Nolle, “Review and Analysis of Solutions of the Three Point Perspective Pose Estimation Problem”, International Journal of Computer Vision, Vol. 13, No. 3, 1994, pp. 331–356.CrossRefGoogle Scholar
  8. 8.
    C. Harris and M. Stephens, “A combined corner and edge detector”, Fourth Alvey Vision Conference, pp. 147–151, 1988.Google Scholar
  9. 9.
    R. Hartley, R. Gupta, T. Chang, “Stereo from uncalibrated cameras”. In Proc. Conf. on Computer Vision and Pattern Recognition, 1992.Google Scholar
  10. 10.
    R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2000.Google Scholar
  11. 11.
    A. Heyden and K. Åström, “Euclidean Reconstruction from Constant Intrinsic Parameters” Proc. 13th International Conference on Pattern Recognition, IEEE Computer Soc. Press, pp. 339–343, 1996.Google Scholar
  12. 12.
    W. Hofmann. Das problem der “Gefährlichen Flächen” in Theorie und Praxis-Ein Beitrag zur Hauptaufgabe der Photogrammetrie. PhD Thesis, Fakultät für Bauwesen, Technische Universityät München, Germany, 1953.Google Scholar
  13. 13.
    F. Kahl, B. Triggs, K. Åström, “Critical Motions for Auto-Calibration When Some Intrinsic Parameters Can Vary”, Journal of Mathematical Imaging and Vision 13, 131–146, 2000.zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Y. Liu, H.-T. Tsui and A. Heyden, “3D Reconstruction of Buildings from an Uncalibrated Image Sequence-A Scene Based Strategy”, Proc. Virtual and Augmented Architecture (VAA’ 01), pp. 231–242, Springer-Verlag, 2001.Google Scholar
  15. 15.
    D. Nister, Automatic Dense Reconstruction from Uncalibrated Video Sequences, Ph. D. dissertation, Dept. of Numerical Analysis and Computing Science, KTH Stockholm, 2001.Google Scholar
  16. 16.
    M. Pollefeys and L. Van Gool, “Stratified Self-Calibration with the Modulus Constraint”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 21, No. 8, pp. 707–724, 1999.CrossRefGoogle Scholar
  17. 17.
    M. Pollefeys, R. Koch and L. Van Gool. “Self-Calibration and Metric Reconstruction in spite of Varying and Unknown Internal Camera Parameters”, International Journal of Computer Vision, 32(1), 7–25, 1999.CrossRefGoogle Scholar
  18. 18.
    M. Pollefeys, Self-calibration and metric 3D reconstruction from uncalibrated image sequences, Ph.D. Thesis, ESAT-PSI, K.U.Leuven, 1999.Google Scholar
  19. 19.
    C. Rother and S. Carlsson. “Linear Multi View Reconstruction and Camera Recovery”, Proc. Eight IEEE International Conference on Computer Vision, pp. 42–49, 2001.Google Scholar
  20. 20.
    P. Sturm. “Critical Motion Sequences for Monocular Self-Calibration and Uncalibrated Euclidean Reconstruction”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 1100–1105, 1997.Google Scholar
  21. 21.
    P. Torr, Motion Segmentation and Outlier Detection, PhD Thesis, Dept. of Engineering Science, University of Oxford, 1995.Google Scholar
  22. 22.
    P. Torr. “An assessment of information criteria for motion model selection”. In CVPR97, pages 47–53, 1997.Google Scholar
  23. 23.
    P. Torr, A. Fitzgibbon and A. Zisserman, “The Problem of Degeneracy in Structure and Motion Recovery from Uncalibrated Image Sequences”, International Journal of Computer Vision, vol. 32, no. 1, pages 27–44, August, 1999.CrossRefGoogle Scholar
  24. 24.
    B. Triggs, “The Absolute Quadric”, Proc. 1997 Conference on Computer Vision and Pattern Recognition, IEEE Computer Soc. Press, pp. 609–614, 1997.Google Scholar
  25. 25.
    B. Triggs, “Autocalibration from planar scenes”, Computer Vision-ECCV’98, vol. 1, Lecture Notes in Computer Science, Vol. 1406, Springer-Verlag, pp 89–105, 1998.CrossRefGoogle Scholar
  26. 26.
    B. Triggs, P. McLauchlan, R. Hartley, and A. Fitzgibbon. “Bundle adjustment-A modern synthesis”. In B. Triggs, A. Zisserman, and R. Szeliski, editors, Vision Algorithms: Theory and Practice, LNCS, pages 298–375. Springer Verlag, 2000.CrossRefGoogle Scholar
  27. 27.
    Z. Zhang, R. Deriche, O. Faugeras and Q.-T. Luong, “A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry”, Artificial Intelligence Journal, Vol. 78, pp. 87–119, October 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Marc Pollefeys
    • 1
  • Frank Verbiest
    • 1
  • Luc Van Gool
    • 1
  1. 1.Center for Processing of Speech and Images (PSI)K.U.LeuvenBelgium

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