3-D stereo using photometric ratios

  • Lawrence B. Wolff
  • Elli Angelopoulou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 801)


We present a novel robust methodology for corresponding a dense set of points on an object surface from photometric values, for 3-D stereo computation of depth. We use two stereo pairs of images, each pair taken of exactly the same scene but under different illumination. By respectively dividing the left images and the right images of these pairs, a stereo pair of photometric ratio images is produced. We formally show that for diffuse reflection the photometric ratio is invariant to camera characteristics, surface albedo, and viewpoint. Therefore the same photometric ratio in both images of a stereo pair implies the same equivalence class of geometric physical constraints. We derive a shape-from-stereo methodology applicable to perspective views and not requiring precise knowledge of illumination conditions. This method is particularly applicable to smooth featureless surfaces. Experimental results of our technique on smooth objects of known ground truth shape are accurate to within 1% depth accuracy.


Diffuse Reflection Specular Reflection Stereo Vision Object Point Stereo Pair 
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.
    N. Ayache. Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception. MIT Press, 1989.Google Scholar
  2. 2.
    A. Blake. “Specular Stereo.” Proceedings of IJCAI, pp. 973–976, 1985.Google Scholar
  3. 3.
    G.J. Brelstaff and A. Blake.“Detecting specular reflections using Lambertian constraints.” Proceedings of the IEEE Second International Conference on Computer Vision (ICCV), pp.297–302, Tampa, Florida, December 1988.Google Scholar
  4. 4.
    D. Clarke and J.F. Grainger. Polarized Light and Optical Measurement. Pergamon Press, 1971.Google Scholar
  5. 5.
    W.E.L. Grimson. From Images to Surfaces: A Computational Study of the Human Early Visual System. MIT Press, 1981.Google Scholar
  6. 6.
    W.E.L. Grimson. “Binocular Shading and Visual Surface Reconstruction.” Computer Vision Graphics and Image Processing, 28 (1): 19–43, 1984.Google Scholar
  7. 7.
    B.K.P. Horn. “Understanding Image Intensities.” Artificial Intelligence, pp. 1–31, 1977.Google Scholar
  8. 8.
    B.K.P. Horn and R.W. Sjoberg. “Calculating the Reflectance Map.” Applied Optics, 18 (11): 1770–1779, June 1979.Google Scholar
  9. 9.
    K. Ikeuchi. “Determining a Depth Map Using a Dual Photometric Stereo.” International Journal of Robotics Research, 6 (1): 15–31, 1987.Google Scholar
  10. 10.
    J.H. Lambert. “Photometria sive de mensura de gratibus luminis, colorum et umbrae” Eberhard Klett. Ausberg, Germany, 1760.Google Scholar
  11. 11.
    D. Marr. Vision. Freeman, San Francisco, 1982.Google Scholar
  12. 12.
    D. Marr and T. Poggio. “A theory of human vision.” Proceedings of the Royal Society of London B, 204: 301–328, 1979.Google Scholar
  13. 13.
    J.E.W. Mayhew and J.P. Frisby. 3D Model Recognition from Stereoscopic Cues. MIT Press, 1991.Google Scholar
  14. 14.
    B.T. Phong. “Illumination for computer generated images.” Communications of the ACM, 18(6): 311–317, June 1975.Google Scholar
  15. 15.
    S. Pollard and J. Mayhew and J. Frisby. “PMF: a stereo correspondence algorithm using the disparity gradient limit.” Perception, 14: 449–470, 1985.Google Scholar
  16. 16.
    R. Siegal and J.R. Howell. Thermal Radiation Heat Transfer. McGraw-Hill, 1981.Google Scholar
  17. 17.
    G.B. Smith. “Stereo Integral Equation.” Proceedings of the AAAI, pp. 689–694, 1986.Google Scholar
  18. 18.
    D. Terzopoulos. “The role of constraints and discontinuities in visible-surface reconstruction.” Proceedings of IJCAI, pp. 1073–1077, 1983.Google Scholar
  19. 19.
    L.B. Wolff. “Diffuse Reflection.” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 472–478, June 1992.Google Scholar
  20. 20.
    L.B. Wolff. “Diffuse and Specular Reflection.” Proceedings of the DARPA Image Understanding Workshop, April 1993.Google Scholar
  21. 21.
    L.B. Wolff and E. Angelopoulou. “3-D Stereo Using Photometric Ratios.” Johns Hopkins University Technical Report CS-93-10, October 1993.Google Scholar
  22. 22.
    L.B. Wolff and T.E. Boult. “Constraining Object Features Using a Polarization Reflectance Model.” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 13 (7): 635–657, July 1991.Google Scholar
  23. 23.
    R.J. Woodham. “Reflectance map techniques for analyzing surface defects in metal castings.” PhD. thesis, MIT AI Lab Tech Report AI-TR-457, June 1978.Google Scholar
  24. 24.
    A. Zisserman and P. Giblin and A. Blake. “The information available to a moving observer from specularities”. Image and Vision Computing, 7 (1): 38–42, 1989.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Lawrence B. Wolff
    • 1
  • Elli Angelopoulou
    • 1
  1. 1.Computer Vision Laboratory Department of Computer ScienceThe Johns Hopkins UniversityBaltimore

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