Automatic camera recovery for closed or open image sequences

  • Andrew W. Fitzgibbon
  • Andrew Zisserman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1406)


We describe progress in completely automatically recovering 3D scene structure together with 3D camera positions from a sequence of images acquired by an unknown camera undergoing unknown movement.

The main departure from previous structure from motion strategies is that processing is not sequential. Instead a hierarchical approach is employed building from image triplets and associated trifocal tensors. This is advantageous both in obtaining correspondences and also in optimally distributing error over the sequence.

The major step forward is that closed sequences can now be dealt with easily. That is, sequences where part of a scene is revisited at a later stage in the sequence. Such sequences contain additional constraints, compared to open sequences, from which the reconstruction can now benefit.

The computed cameras and structure are the backbone of a system to build texture mapped graphical models directly from image sequences.


Projection Matrice Bundle Adjustment Closed Sequence Reprojection Error Trifocal Tensor 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    N. Ayache. Artificial vision for mobile robots. MIT Press, Cambridge, 1991.Google Scholar
  2. 2.
    P. Beardsley, P. Torr, and A. Zisserman. 3D model acquisition from extended image sequences. In Proc. ECCV, LNCS 1064/1065, pages 683–695. Springer-Verlag, 1996.Google Scholar
  3. 3.
    P. Beardsley, A. Zisserman, and D. W. Murray. Navigation using affine structure and motion. In Proc. ECCV, LNCS 800/801, pages 85–96. Springer-Verlag, 1994.Google Scholar
  4. 4.
    C. J. Harris. Determination of ego-motion from matched points. In Alvey Vision Conf., pages 189–192, 1987.Google Scholar
  5. 5.
    C. J. Harris and M. Stephens. A combined corner and edge detector. In Alvey Vision Conf., pages 147–151, 1988.Google Scholar
  6. 6.
    R. I. Hartley. Euclidean reconstruction from uncalibrated views. In J. Mundy, A. Zisserman, and D. Forsyth, editors, Applications of Invariance in Computer Vision, LNCS 825, pages 237–256. Springer-Verlag, 1994.Google Scholar
  7. 7.
    R. I. Hartley. A linear method for reconstruction from lines and points. In Proc. ICCV, pages 882–887, 1995.Google Scholar
  8. 8.
    R. I. Hartley and P. Sturm. Triangulation. In American Image Understanding Workshop, pages 957–966, 1994.Google Scholar
  9. 9.
    A. Heyden and K. åström. Euclidean reconstruction from image sequences with varying and unknown focal length and principal point. In Proc. CVPR, 1997.Google Scholar
  10. 10.
    D. Jacobs. Linear fitting with missing data: Applications to structure from motion and to characterizing intensity images. In Proc. CVPR, pages 206–212, 1997.Google Scholar
  11. 11.
    S. Laveau. Géométrie d'un système de N caméras. Théorie, estimation et applications. PhD thesis, INRIA, 1996.Google Scholar
  12. 12.
    S. J. Maybank and A. Shashua. Ambiguity in reconstruction from images of six points. In Proc. ICCV, pages 703–708, 1998.Google Scholar
  13. 13.
    P. F. McLauchlan and D. W. Murray. A unifying framework for structure from motion recovery from image sequences. In Proc. ICCV, pages 314–320, 1995.Google Scholar
  14. 14.
    P. F. McLauchlan, I. D. Reid, and D. W. Murray. Recursive affine structure and motion from image sequences. In Proc. ECCV, volume 1, pages 217–224, May 1994.Google Scholar
  15. 15.
    R. Mohr, B. Boufama, and P. Brand. Accurate projective reconstruction. In J. Mundy, A. Zisserman, and D. Forsyth, editors, Applications of Invariance in Computer Vision, LNCS 825. Springer-Verlag, 1994.Google Scholar
  16. 16.
    M. Pollefeys, R. Koch, and L. Van Gool. Self calibration and metric reconstruction in spite of varying and unknown internal camera parameters. In Proc. ICCV, pages 90–96, 1998.Google Scholar
  17. 17.
    J. Porrill. Optimal combination and constraints for geometrical sensor data. Intl. J. of Robotics Research, 7(6):66–77, 1988.Google Scholar
  18. 18.
    I. D. Reid and D. W. Murray. Active tracking of foveated feature clusters using affine structure. Intl. J. of Computer Vision, 18(1):41–60, 1996.CrossRefGoogle Scholar
  19. 19.
    C. Schmid and A. Zisserman. Automatic line matching across views. In Proc. CVPR, pages 666–671, 1997.Google Scholar
  20. 20.
    A. Shashua. Trilinearity in visual recognition by alignment. In Proc. ECCV, volume 1, pages 479–484, May 1994.MathSciNetGoogle Scholar
  21. 21.
    H. Y. Shum, M. Hebert, K. Ikeuchi, and R. Reddy. An integral approach to free-form object modeling. In Proc. ICCV, pages 870–875, 1995.Google Scholar
  22. 22.
    G. Sparr. Simultaneous reconstruction of scene structure and camera locations from uncalibrated image sequences. In Proc. ICPR, 1996.Google Scholar
  23. 23.
    M. E. Spetsakis and J. Aloimonos. Structure from motion using line correspondences. Intl. J. of Computer Vision, 4(3):171–183, 1990.CrossRefGoogle Scholar
  24. 24.
    P. Sturm. Vision 3D non calibrée: Contributions à la reconstruction projective et étude des mouvements critiques pour l'auto calibrage. PhD thesis, INRIA RhÔne-Alpes, 1997.Google Scholar
  25. 25.
    P. Sturm and W. Triggs. A factorization based algorithm for multi-image projective structure and motion. In Proc. ECCV, pages 709–720, 1996.Google Scholar
  26. 26.
    C. Tomasi and T. Kanade. Shape and motion from image streams under orthography: A factorization approach. Intl. J. of Computer Vision, 9(2):137–154, 1992.CrossRefGoogle Scholar
  27. 27.
    P. H. S. Torr, A. W. Fitzgibbon, and A. Zisserman. Maintaining multiple motion model hypotheses over many views to recover matching and structure. In Proc. ICCV, pages 485–491, January 1998.Google Scholar
  28. 28.
    P. H. S. Torr and D. W. Murray. Statistical detection of independent movement from a moving camera. Image and Vision Computing, 1(4):180–187, May 1993.CrossRefGoogle Scholar
  29. 29.
    P. H. S. Torr and D. W. Murray. The development and comparison of robust methods for estimating the fundamental matrix. Intl. J. of Computer Vision, 24(3):271–300, 1997.CrossRefGoogle Scholar
  30. 30.
    P. H. S. Torr and A. Zisserman. Robust parameterization and computation of the trifocal tensor. Image and Vision Computing, 15:591–605, 1997.CrossRefGoogle Scholar
  31. 31.
    W. Triggs. Auto-calibration and the absolute quadric. In Proc. CVPR, pages 609–614, 1997.Google Scholar
  32. 32.
    Z. Zhang, R. Deriche, O. Faugeras, and Q. Luong. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence, 78:87–119, 1995.CrossRefGoogle Scholar
  33. 33.
    Z. Zhang and O. Faugeras. 3D Dynamic Scene Analysis. Springer-Verlag, 1992.Google Scholar
  34. 34.
    A. Zisserman, P. Beardsley, and I. Reid. Metric calibration of a stereo rig. In IEEE Workshop on Representation of Visual Scenes, Boston, pages 93–100, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Andrew W. Fitzgibbon
    • 1
  • Andrew Zisserman
    • 1
  1. 1.Robotics Research Group, Department of Engineering ScienceUniversity of OxfordOxfordUK

Personalised recommendations