A Method for Estimating Illumination Distribution of a Real Scene Based on Soft Shadows

  • Imari Sato
  • Yoichi Sato
  • Katsushi Ikeuchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1554)


This paper describes a new method for estimating an illumination distribution of a real scene. Shadows in a real scene are usually observed as soft shadows that do not have sharp edges. In the proposed method, illumination distribution of the real scene is estimated based on radiance distribution inside the soft shadows cast by an object in the scene. By observing shadows and not illumination itself, the proposed method is able to avoid several technical problems which the previously proposed methods suffered from: how to capture a wide field of view of the entire scene and how to capture a high dynamic range of the illumination. The estimated illumination distribution is then used for rendering virtual objects superimposed onto images of the real scene. We successfully tested the proposed method by using real images to demonstrate its effectiveness.


Augmented Reality Virtual Object High Dynamic Range Shadow Image Real Scene 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Azuma, R.T.: A survey of augmented reality. Presence: Teleoperators and Virtual Environments, vol. 6, no. 4. (1997) 355–385Google Scholar
  2. 2.
    Azuma, R.T., Bishop, G.: Improving static and dynamic registration in an optical see-through HMD. Proceedings of SIGGRAPH 94. (1994) 197–204Google Scholar
  3. 3.
    Bajura, M., Fuchs, H., and Ohbuchi, R.: Merging virtual objects with the real world: seeing ultrasound imagery within the patient. Proceedings of SIGGRAPH 92. (1992) 203–210Google Scholar
  4. 4.
    Cohen, M.F., Chen, S.E., Wallace, J.R., Greenberg, D.P.: A progressive Refinement Approach to Fast Radiosity Image Generation Proceedings of SIGGRAPH 88. (1998) 75–84Google Scholar
  5. 5.
    Debevec, P.E.: Rendering Synthetic Objects into Real Scenes: Bridging Traditional and Image-based Graphics with Global Illumination and High Dynamic Range Photography. Proceedings of SIGGRAPH 98. (1998) 189–198Google Scholar
  6. 6.
    Drettakis, G., Robert, L., Bougnoux, S.: Interactive Common Illumination for Computer Augmented Reality. Proceedings of 8th Eurographics Workshop on Rendering. (1997) 45–57Google Scholar
  7. 7.
    Fournier, A., Gunawan, A., Romanzin, C.: Common Illumination between Real and Computer Generated Scenes. Proceedings of Graphics Interface 93. (1993) 254–262Google Scholar
  8. 8.
    Horn, B.K.P.: Robot Vision. The MIT Press, Cambridge, MA, (1986)Google Scholar
  9. 9.
    Kanade, T., Yoshida, A., Oda, K., Kano, H., Tanaka, M.: A Video-Rate Stereo Machine and Its New Applications. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 96. (1996) 196–202Google Scholar
  10. 10.
    Nayar, S.K., Ikeuchi, K., Kanade, T.: Surface reflection: physical and geometrical perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no.7. (1991) 611–634CrossRefGoogle Scholar
  11. 11.
    Sato, I., Sato, Y., Ikeuchi K.: Acquiring a radiance distribution to superimpose virtual objects into a real scene. To appear in IAPR Workshop Machine Vision and Application. (1998)Google Scholar
  12. 12.
    State, A., Hirota, G., Chen, D.T., Garrett, W.F., Livingston, M.A., Superior augmented-reality registration by integrating landmark tracking and magnetic tracking. Proceedings of SIGGRAPH 96. (1996) 429–438Google Scholar
  13. 13.
    Torrance, K.E., Sparrow, E.M.: Theory for off-specular reflection from roughened surface. Journal of Optical Society of America, vol.57. (1967) 1105–1114CrossRefGoogle Scholar
  14. 14.
    Tsai, R.: A Versatile Camera Calibration Technique for High Accuracy Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses. IEEE Journal of Robotics and Automation, vol. 3, no. 4. (1987) 323–344Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Imari Sato
    • 1
  • Yoichi Sato
    • 1
  • Katsushi Ikeuchi
    • 1
  1. 1.Institute of Industrial ScienceThe University of TokyoTokyoJapan

Personalised recommendations