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Structure from Many Perspective Images with Occlusions

  • Daniel Martinec
  • Tomáš Pajdla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2351)

Abstract

This paper proposes a method for recovery of projective shape and motion from multiple images by factorization of a matrix containing the images of all scene points. Compared to previous methods, this method can handle perspective views and occlusions jointly. The projective depths of image points are estimated by the method of Sturm & Triggs [11] using epipolar geometry. Occlusions are solved by the extension of the method by Jacobs [8] for filling of missing data. This extension can exploit the geometry of perspective camera so that both points with known and unknown projective depths are used. Many ways of combining the two methods exist, and therefore several of them have been examined and the one with the best results is presented. The new method gives accurate results in practical situations, as demonstrated here with a series of experiments on laboratory and outdoor image sets. It becomes clear that the method is particularly suited for wide base-line multiple view stereo.

Keywords

projective reconstruction structure from motion wide baseline stereo factorization 

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References

  1. 1.
    D. Martinec and T. Pajdla. Structure from Many Perspective Images with Occlusions. Research Report CTU-CMP-2001-20, Center for Machine Perception, K333 FEE Czech Technical University, Prague, Czech Republic, July 2001. ftp://cmp.felk.cvut.cz/pub/cmp/articles/martinec/Martinec-TR-2001-20.pdf.Google Scholar
  2. 2.
    M. K. Bennett. Affine and Projective Geometry. John Willey and Sons, New York, USA, 1995.zbMATHGoogle Scholar
  3. 3.
    A. W. Fitzgibbon and A. Zisserman. Automatic camera recovery for closed or open image sequences. In Proc. European Conference on Computer Vision, pages 311–326. Springer-Verlag, June 1998.Google Scholar
  4. 4.
    C. J. Harris and M. Stephens. A combined corner and edge detector. In Proc. of Alvey Vision Conference, pages 147–151, 1988.Google Scholar
  5. 5.
    R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge, UK, 2000.zbMATHGoogle Scholar
  6. 6.
    A. Heyden. Projective structure and motion from image sequences using subspace methods. In Proc. 10th SCIA, pages 963–968, June 1997.Google Scholar
  7. 7.
    D. Q. Huynh and A. Heyden. Outlier Detection in Video Sequences under Affine Projection. In Proc. of CVPR, 2001.Google Scholar
  8. 8.
    D. Jacobs. Linear fitting with missing data: Applications to structure from motion and to characterizing intensity images. In CVPR, pages 206–212, 1997.Google Scholar
  9. 9.
    S. Mahamud and M. Hebert. Iterative projective reconstruction from multiple views. In CVPR, 2000.Google Scholar
  10. 10.
    S. Avidan and A. Shashua. Threading Fundamental Matrices. In IEEE Trans. on PAMI, Vol. 23(1), pp. 73–77, 2001.Google Scholar
  11. 11.
    P. Sturm and B. Triggs. A factorization based algorithm for multi-image projective structure and motion. In ECCV96(II), pages 709–720, 1996.Google Scholar
  12. 12.
    C. Tomasi and T. Kanade. Shape and motion from image streams under orthography: A factorization method. In IJCV(9), No. 2, pages 137–154, November 1992.Google Scholar
  13. 13.
    M. Urban, T. Pajdla, and V. Hlaváč. Projective reconstruction from N views having one view in common. In Vision Algorithms: Theory & Practice. Springer LNCS 1883, pages 116–131, September 1999.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Daniel Martinec
    • 1
  • Tomáš Pajdla
    • 1
  1. 1.Center for Machine Perception Department of CyberneticsCzech Technical University in PraguePrahaCzech Republic

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