Advertisement

Out of the dark: Using shadows to reconstruct 3D surfaces

  • M. Daum
  • G. Dudek
Session T1B: Physics-Based Vision
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1351)

Abstract

Shape From Darkness refers to using the shadows cast by a scene to reconstruct the structure of the scene. A collection of images associated with different light source positions is used. Previously published solutions to this problem have performed the reconstruction only for cross sections of the scene.

We propose a variant of Shape From Darkness which is capable of reconstructing the entire 3-D scene. In addition, this algorithm can be applied to a broader class of light source trajectories, including trajectories which mimic the motion of the sun during the day.

We present a formal statement of the 3-D problem and some of its characteristics, and an algorithm for recovering a surface from shadows. Experimental results are presented and discussed for both real data and synthetic data with associated ground truth.

Keywords

Shape From Darkness 3D Reconstruction Scene Recovery Shadows 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    K.A. Loparo D. Raviv, Y. Pao. Reconstruction of three-dimensional surfaces from two-dimensional binary images. IEEE Transactions on Robotics and Automation, 5(5):701–710, 1989.Google Scholar
  2. 2.
    J. Kender D. Yang. Shape from shadows under error. In Image Understanding Workshop 1993, pages 1083–1090, Washington, D.C., August 1993.Google Scholar
  3. 3.
    P. Dupuis and J. Oliensis. Direct method for reconstructing shape from shading. pages 453–458.Google Scholar
  4. 4.
    P. Dupuis and J. Oliensis. Shape from shading: Provably convergent algorithms and uniqueness results. volume 2, pages 259–268.Google Scholar
  5. 5.
    Robert T. Frankot and Rama Chellappa. A method for enforcing integrability in shape from shading algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence, 10(4):439–451, July 1988.Google Scholar
  6. 6.
    M. Hatzitheodorou and J.R. Kender. An optimal algorithm for the derivation of shape from shadows.Google Scholar
  7. 7.
    Berthold Horn. Robot Vision. The MIT Press, Cambridge, Massachusetts, 1986.Google Scholar
  8. 8.
    Katsushi Ikeuchi and Berthold K. P. Horn. Numerical shape from shading and occluding boundaries. In Michael Brady, editor, Computer Vision, pages 141–184. Elsevier Science Publishing Company, New York, NY, August 1981.Google Scholar
  9. 9.
    E.M. Smith J.R. Kender. Shape from darkness: Deriving surface information from dynamic shadows. In AIII, pages 539–546, 1987.Google Scholar
  10. 10.
    Michael Langer, Gregory Dudek, and Steven W. Zucker. Space occupancy using multiple shadowimages. In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 390–396, Pittsburgh, PA, August 1995. IEEE Press.Google Scholar
  11. 11.
    M.S. Langer and S. W. Zucker. Shape-from-shading on a cloudy day. Journal of the Optical Society of America A, 11(2):467–478, 1994.Google Scholar
  12. 12.
    Alex P. Pentland. Local shading analysis. IEEE Trans. Pattern Analysis and Machine Intelligence, 6(2):170–187, March 1984.Google Scholar
  13. 13.
    Andrei Nikolaevich Tikhonov and Vasilii Iakovlevich Arsenin. Solutions of ill-posed problems [Metody resheniia nekorrektnykh zadach]. Halsted Press, New York, 1977.Google Scholar
  14. 14.
    James T. Todd and Ennio Mingolla. Perception of surface curvature and direction of illumination from patterns of shading. Journal of Experimental Psychology: Human Perception and Performance, 9(4):583–595, 1983. *** DIRECT SUPPORT *** A0008188 00004Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • M. Daum
    • 1
  • G. Dudek
    • 1
  1. 1.Centre For Intelligent Machines School of Computer ScienceMcGill UniversityMontrealCanada

Personalised recommendations