Structure and Motion from Images of Smooth Textureless Objects

  • Yasutaka Furukawa
  • Amit Sethi
  • Jean Ponce
  • David Kriegman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3022)


This paper addresses the problem of estimating the 3D shape of a smooth textureless solid from multiple images acquired under orthographic projection from unknown and unconstrained viewpoints. In this setting, the only reliable image features are the object’s silhouettes, and the only true stereo correspondence between pairs of silhouettes are the frontier points where two viewing rays intersect in the tangent plane of the surface. An algorithm for identifying geometrically-consistent frontier points candidates while estimating the cameras’ projection matrices is presented. This algorithm uses the signature representation of the dual of image silhouettes to identify promising correspondences, and it exploits the redundancy of multiple epipolar geometries to retain the consistent ones. The visual hull of the observed solid is finally reconstructed from the recovered viewpoints. The proposed approach has been implemented, and experiments with six real image sequences are presented, including a comparison between ground-truth and recovered camera configurations, and sample visual hulls computed by the algorithm.


Motion Estimation Projection Matrix Projection Matrice Epipolar Line Epipolar Geometry 
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.


  1. 1.
    Sethi, A., Renaudie, D., Kriegman, D., Ponce, J.: Curve and Surface Duals and the Recognition of Curved 3D Objects from their Silhouette. Int. J. of Comp. Vision 58(1) (2004)Google Scholar
  2. 2.
    Åström, K., Kahl, F.: Motion estimation in image sequences using the deformation of apparent contours. IEEE Trans. Patt. Anal. Mach. Intell 21(2), 114–127 (1999)CrossRefGoogle Scholar
  3. 3.
    Baumgart, B.G.: Geometric modeling for computer vision. Technical Report AIM- 249, Stanford University, Ph.D. Thesis. Department of Computer Science (1974)Google Scholar
  4. 4.
    Birchfield, S.: KLT: An implementation of the Kanade-Lucas-Tomasi feature trackerGoogle Scholar
  5. 5.
    Boyer, E., Berger, M.O.: 3d surface reconstruction using occluding contours. Int. J. of Comp. Vision 22(3), 219–233 (1997)CrossRefGoogle Scholar
  6. 6.
    Cipolla, R., Åström, K.E., Giblin, P.J.: Motion from the frontier of curved surfaces. In: Proc. Int. Conf. Comp. Vision, pp. 269–275 (1995)Google Scholar
  7. 7.
    Cipolla, R., Blake, A.: Surface shape from the deformation of apparent contours. Int. J. of Comp. Vision 9(2), 83–112 (1992)CrossRefGoogle Scholar
  8. 8.
    Faugeras, O., Luong, Q.-T., Papadopoulo, T.: The Geometry of Multiple Images. MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  9. 9.
    Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Prentice-Hall, Englewood Cliffs (2002)Google Scholar
  10. 10.
    Giblin, P., Weiss, R.: Epipolar curves on surfaces. Image and Vision Computing 13(1), 33–44 (1995)CrossRefGoogle Scholar
  11. 11.
    Giblin, P., Pollick, F.E., Rycroft, J.E.: Recovery of an unknown axis of rotation from the profiles of a rotating surface. Journal of Optical Society America, 1976–1984 (1994)Google Scholar
  12. 12.
    Giblin, P., Weiss, R.: Reconstruction of surface from profiles. In: Proc. Int. Conf. Comp. Vision, pp. 136–144 (1987)Google Scholar
  13. 13.
    Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  14. 14.
    Joshi, T., Ahuja, N., Ponce, J.: Structure and motion estimation from dynamic silhouettes under perspective projection. In: Proc. Int. Conf. Comp. Vision, pp. 290–295 (1995)Google Scholar
  15. 15.
    Koenderink, J.J.: What does the occluding contour tell us about solid shape? Perception 13, 321–330 (1984)CrossRefGoogle Scholar
  16. 16.
    Laurentini. How far 3D shapes can be understood from 2D silhouettes. IEEE Trans. Patt. Anal. Mach. Intell., 17(2):188–194, February 1995. Google Scholar
  17. 17.
    Levi, N., Werman, M.: The viewing graph. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 518–522 (2003)Google Scholar
  18. 18.
    Mendonca, P., Wong, K.-Y.K., Cipolla, R.: Camera pose estimation and reconstruction from image profiles under circular motion. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 864–877. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  19. 19.
    Poelman, C.J., Kanade, T.: A paraperspective factorization method for shape and motion recovery. IEEE Trans. Patt. Anal. Mach. Intell. 19(3), 206–218 (1997)CrossRefGoogle Scholar
  20. 20.
    Szeliski, R., Weiss, R.: Robust shape recovery from occluding contours using a linear smoother. Int. J. of Comp. Vision 28(1), 27–44 (1998)CrossRefGoogle Scholar
  21. 21.
    Tomasi, C., Kanade, T.: Shape and motion from image streams under orthography: a factorization method. Int. J. of Comp. Vision 9(2), 137–154 (1992)CrossRefGoogle Scholar
  22. 22.
    Torr, P., Murray, D.: The development and comparison of robust methods for estimating the fundamental matrix. Int. J. of Comp. Vision 24(3) (1997)Google Scholar
  23. 23.
    Torr, P.H., Zisserman, A., Maybank, S.J.: Robust detection of degenerate configurations for the fundamental matrix. In: Proc. Int. Conf. Comp. Vision, Boston, MA, pp. 1037–1042 (1995)Google Scholar
  24. 24.
    Torr, P.H.S., Zisserman, A.: Mlesac: A new robust estimator with application to estimating image geometry. CVIU 78(1), 138–156 (2000)Google Scholar
  25. 25.
    Vaillant, R., Faugeras, O.D.: Using extremal boundaries for 3-d object modeling. IEEE Trans. Patt. Anal. Mach. Intell. 14(2), 157–173 (1992)CrossRefGoogle Scholar
  26. 26.
    Vijayakumar, B., Kriegman, D.J., Ponce, J.: Structure and motion of curved 3d objects from monocular silhouettes. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 327–334 (1996)Google Scholar
  27. 27.
    Wang, Y., Teoh, E.K., Shen, D.: Structure-adaptive b-snake for segmenting complex objects. In: IEEE International Conference on Image Processing (2001)Google Scholar
  28. 28.
    Wong, K.-Y.K., Cipolla, R.: Structure and motion from silhouettes. In: Proc. Int. Conf. Comp. Vision, pp. 217–222 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Yasutaka Furukawa
    • 1
  • Amit Sethi
    • 1
  • Jean Ponce
    • 1
  • David Kriegman
    • 2
  1. 1.Beckman InstituteUniversity of Illinois at Urbana-Champaign 
  2. 2.Dept. of Computer ScienceUniversity of California at San Diego 

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