Shape and Reflectance from Natural Illumination

  • Geoffrey Oxholm
  • Ko Nishino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)


We introduce a method to jointly estimate the BRDF and geometry of an object from a single image under known, but uncontrolled, natural illumination. We show that this previously unexplored problem becomes tractable when one exploits the orientation clues embedded in the lighting environment. Intuitively, unique regions in the lighting environment act analogously to the point light sources of traditional photometric stereo; they strongly constrain the orientation of the surface patches that reflect them. The reflectance, which acts as a bandpass filter on the lighting environment, determines the necessary scale of such regions. Accurate reflectance estimation, however, relies on accurate surface orientation information. Thus, these two factors must be estimated jointly. To do so, we derive a probabilistic formulation and introduce priors to address situations where the reflectance and lighting environment do not sufficiently constrain the geometry of the object. Through extensive experimentation we show what this space looks like, and offer insights into what problems become solvable in various categories of real-world natural illumination environments.


Lighting Environment Surface Patch Surface Orientation Angular Error Photometric Stereo 
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.


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  1. 1.
    Alldrin, N.G., Kriegman, D.J.: Toward Reconstructing Surfaces With Arbitrary Isotropic Reflectance: A Stratified Photometric Stereo Approach. In: IEEE Int’l Conf. on Computer Vision, pp. 1–8 (2007)Google Scholar
  2. 2.
    Alldrin, N.G., Zickler, T., Kriegman, D.J.: Photometric stereo with non-parametric and spatially-varying reflectance. In: IEEE Int’l Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  3. 3.
    Basri, R., Jacobs, D., Kemelmacher, I.: Photometric Stereo with General, Unknown Lighting. Int’l Journal of Computer Vision 72(3), 239–257 (2006)CrossRefGoogle Scholar
  4. 4.
    Boykov, Y., Veksler, O., Zabih, R.: Fast Approximate Energy Minimization via Graph Cuts. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)CrossRefGoogle Scholar
  5. 5.
    Debevec, P.: Light Probe Image Gallery (2012),
  6. 6.
    Durou, J.-D., Falcone, M., Sagona, M.: Numerical Methods for Shape-from-Shading: A New Survey with Benchmarks. Computer Vision and Image Understanding 109(1), 22–43 (2008)CrossRefGoogle Scholar
  7. 7.
    Goldman, D.B., Curless, B., Hertzmann, A., Seitz, S.M.: Shape and Spatially-Varying BRDFs from Photometric Stereo. IEEE Trans. on Pattern Analysis and Machine Intelligence 32(6), 1060–1071 (2010)CrossRefGoogle Scholar
  8. 8.
    Hertzmann, A., Seitz, S.M.: Example-Based Photometric Stereo: Shape Reconstruction with General, Varying BRDFs. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(8), 1254–1264 (2005)CrossRefGoogle Scholar
  9. 9.
    Huang, R., Smith, W.: Shape-from-Shading Under Complex Natural Illumination. In: IEEE Int’l Conf. on Image Processing, pp. 13–16 (2011)Google Scholar
  10. 10.
    Ikeuchi, K., Horn, B.K.P.: Numerical Shape from Shading and Occluding Boundaries. Artificial Intelligence (1981)Google Scholar
  11. 11.
    Johnson, M.K., Adelson, E.H.: Shape Estimation in Natural Illumination. In: IEEE Int’l Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2011)Google Scholar
  12. 12.
    Kim, J., Zabih, R.: Factorial Markov Random Fields. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part III. LNCS, vol. 2352, pp. 321–334. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Lombardi, S., Nishino, K.: Single Image Multimaterial Estimation. In: IEEE Int’l Conf. on Computer Vision and Pattern Recognition, pp. 238–245 (2011)Google Scholar
  14. 14.
    Matusik, W., Pfister, H., Brand, M., McMillan, L.: A data-driven reflectance model. ACM Trans. on Graphics 22(3), 759–769 (2003)CrossRefGoogle Scholar
  15. 15.
    Nishino, K.: Directional Statistics BRDF Model. In: IEEE Int’l Conf. on Computer Vision, pp. 476–483 (2009)Google Scholar
  16. 16.
    Nishino, K., Lombardi, S.: Directional Statistics-based Reflectance Model for Isotropic Bidirectional Reflectance Distribution Functions. Journal of Optical Society America, A 28(1), 8–18 (2011)CrossRefGoogle Scholar
  17. 17.
    Rusinkiewicz, S.: A New Change of Variables for Efficient BRDF Representation. In: Eurographics Workshop on Rendering, pp. 11–22 (1998)Google Scholar
  18. 18.
    Woodham, R.J.: Photometric Method for Determining Surface Orientation from Multiple Images, vol. 19. MIT Press (1989)Google Scholar
  19. 19.
    Zhang, R., Tsai, P.-S., Cryer, J.E., Shah, M.: Shape from Shading: A Survey. IEEE Trans. on Pattern Analysis and Machine Intelligence 21(8), 690–706 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Geoffrey Oxholm
    • 1
  • Ko Nishino
    • 1
  1. 1.Department of Computer ScienceDrexel UniversityPhiladelphiaUSA

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