Multi-camera Radiometric Surface Modelling for Image-Based Re-lighting

  • Oliver Grau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


This contribution describes an automatic method to retrieve the diffuse radiometric surface model of moving persons or other objects along with the object geometry using a multi-camera system. The multi-camera equipped studio allows synchronised capture of the foreground action and a visual hull computation is then used to compute a 3D model of that scene. The diffuse surface reflection parameters are computed using the 3D model from that process together with an illumination map of the studio. The illumination map is a high dynamic range image generated from a series of images of the studio using a camera equipped with a spherical (fish-eye) lens. With this setup our method is able to capture any action in the studio under normal lighting.


Foreground Object Visual Hull Global Illumination High Dynamic Range Image Optical Transfer Function 
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.
    Debevec, P.E.: Rendering synthetic objects into real scenes: Bridging traditional and image-based graphics with global illumination and high dynamic range photography. In: Proceedings of SIGGRAPH 1998, Computer Graphics Proceedings, Annual Conference Series, Orlando, USA, pp. 189–198 (1998)Google Scholar
  2. 2.
    Sato, Y., Wheeler, M.D., Ikeuchi, K.: Object shape and reflectance modeling from observation. Computer Graphics (Annual Conference Series) 31, 379–388 (1997)Google Scholar
  3. 3.
    Marschner, S.R., Westin, S.H., Lafortune, E.P.F., Torrance, K.E., Greenberg, D.P.: Image-based brdf measurement including human skin. In: Proceedings of 10th Eurographics Workshop on Rendering, Granada, Spain, pp. 139–152 (1999)Google Scholar
  4. 4.
    Lensch, H., Kautz, J., Goesele, M., Heidrich, W., Seidel, H.P.: Image-based reconstruction of spatial appearance and geometric detail. ACM Transactions on Graphics 22(2), 234–257 (2003)CrossRefGoogle Scholar
  5. 5.
    Debevec, P., Hawkins, T., Tchou, C., Duiker, H., Sarokin, W., Sagar, M.: Acquiring the reflectance field of a human face. In: Proceedings of SIGGRAPH 2000, Computer Graphics Proceedings. Annual Conference Series (2000)Google Scholar
  6. 6.
    Grau, O., Pullen, T., Thomas, G.A.: A combined studio production system for 3-D capturing of live action and immersive actor feedback. IEEE Transactions on Circuits and Systems for Video Technology 14(3), 370–380 (2004)CrossRefGoogle Scholar
  7. 7.
    Potmesil, M.: Generating octree models of 3D objects from their silhouettes in a sequence of images. Computer Vision, Graphics and Image Processing 40, 1–29 (1987)CrossRefGoogle Scholar
  8. 8.
    Szeliski, R.: Rapid octree construction from image sequences. CVGIP: Image Understanding 58(1), 23–32 (1993)CrossRefGoogle Scholar
  9. 9.
    Grau, O.: 3d sequence generation from multiple cameras. In: Proc. of IEEE, International Workshop on Multimedia Signal Processing 2004, Siena, Italy (2004)Google Scholar
  10. 10.
    Grossberg, M., Nayar, S.: Modeling the Space of Camera Response Functions. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(10), 1272–1282 (2004)CrossRefGoogle Scholar
  11. 11.
    Debevec, P., Malik, J.: Recovering high dynamic range radiance maps from photographs. In: Proceedings of SIGGRAPH 1997, pp. 369–378 (1997)Google Scholar
  12. 12.
    Grau, O.: Capturing of light and surface reflection properties for re-lighting (2006),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Oliver Grau
    • 1
  1. 1.BBC ResearchKingswood Warren, TadworthUK

Personalised recommendations