Lighting Reconstruction Using Fast and Adaptive Density Estimation Techniques

  • Karol Myszkowski
Part of the Eurographics book series (EUROGRAPH)


Monte Carlo (MC) photon shooting approach is becoming an important global illumination technique in research and commercial applications. In this work, we focus on the problem of lighting reconstruction for planar surfaces. Our contribution is in the development of new, efficient photon density estimation techniques. We formulate local error measures of lighting reconstruction which under some reasonable constraints (discussed below) imposed on the lighting function that behave like the actual error. The minimization of our error estimates is very fast for planar surfaces and usually leads to a better quality lighting result than traditional methods. Also, the local error estimation offers more information than global error measures usually provided by MC solvers, which are not good predictors of image quality. We compare the actual error resulting from various techniques, and evaluate the visual appearance of the reconstructed lighting.


Monte Carlo Near Neighbor Photon Density Global Illumination Variable Kernel 
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.
    A.C. Bovik, T.S. Huang, and D.C. Munson. The effect of median filtering on edge estimation and detection. Pattern Analysis and Machine Intelligence, 9 (2): 181–194, 1987.CrossRefGoogle Scholar
  2. 2.
    S. E. Chen, H. E. Rushmeier, G. Miller, and D. Turner. A progressive multi-pass method for global illumination. Comp. Graphics, 25 (4): 164–74, 1991.CrossRefGoogle Scholar
  3. 3.
    S. Collins. Adaptive Splatting for Specular to Diffuse Light Transport. Fifth Eurographics Workshop on Rendering, pages 119–135, June 1994.Google Scholar
  4. 4.
    F. C. Crow. Summed-area tables for texture mapping. Computer Graphics, 18: 207–212, July 1984.CrossRefGoogle Scholar
  5. 5.
    B. Efron. The Jacknife, the Bootstrap, and Other Resampling Plans. S.I.A.M., Philadelphia, 1982.Google Scholar
  6. 6.
    P. Heckbert. Adaptive radiosity textures for bidirectional ray tracing. Computer Graphics (SIGGRAPH’90 Proceedings), 24 (4): 145–154, August 1990.CrossRefGoogle Scholar
  7. 7.
    H. W. Jensen. Global Illumination Using Photon Maps. Seventh Eurographics Workshop on Rendering, pages 21–30, 1996.Google Scholar
  8. 8.
    A. B. Khodulev. Comparison of two methods of global illumination analysis. In,1996.Google Scholar
  9. 9.
    A. B. Khodulev and E. A. Kopylov. Physically accurate lighting simulation in computer graphics software. In Graphicon’96, pages 111–119, 1996.Google Scholar
  10. 10.
    K. Myszkowski. http: // jp/-k-myszk/density— the web page accompanying this paper.Google Scholar
  11. 11.
    P. Shirley, B. Wade, P. M. Hubbard, D. Zareski, B. Walter, and D. P. Greenberg. Global Illumination via Density Estimation. In Sixth Eurographics Workshop on Rendering, pages 219–230, 1995.Google Scholar
  12. 12.
    B.W. Silverman. Density Estimation for Statistics and Data Analysis. Chapmann and Hall, London, 1985.Google Scholar

Copyright information

© Springer-Verlag/Wien 1997

Authors and Affiliations

  • Karol Myszkowski
    • 1
  1. 1.The University of AizuAizu-WakamatsuJapan

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