Thrifty Final Gather for Radiosity

  • Annette Scheel
  • Marc Stamminger
  • Hans-Peter Seidel
Part of the Eurographics book series (EUROGRAPH)


Finite Element methods are well suited to the computation of the light distribution in mostly diffuse scenes, but the resulting mesh is often far from optimal to accurately represent illumination. Shadow boundaries are hard to capture in the mesh, and the illumination may contain artifacts due to light transports at different mesh hierarchy levels. To render a high quality image a costly final gather reconstruction step is usually done, which re-evaluates the illumination integral for each pixel. In this paper an algorithm is presented which significantly speeds up the final gather by exploiting spatial and directional coherence information taken from the radiosity solution. Senders are classified, so that their contribution to a pixel is either interpolated from the radiosity solution or recomputed with an appropriate number of new samples. By interpolating this sampling pattern over the radiosity mesh, continuous solutions are obtained.


Form Factor Global Illumination Shadow Boundary Test Scene Indirect Light 
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]
    Philippe Bekaert, Philip Dutre, and Yves Willems. Final radiosity gather step using a monte-carlo technique with optimal importance sampling. Technical Report CW275, Katholike Univ. Leuven, 1996.Google Scholar
  2. [2]
    Per H. Christensen, Dani Lischinski, Eric Stollnitz, and David H. Salesin. Clustering for glossy global illumination. ACM Transactions on Graphics, 16(1):3–33, January 1997.CrossRefGoogle Scholar
  3. [3]
    CIE. Recommendations on uniform color spaces-color difference equations-psychometric color terms. Technical Report 15.2, CIE, 1996.Google Scholar
  4. [4]
    J. Dischler, L. Moustefaoui, and D. Ghazanfarpour. Radiosity including complex surfaces and geometric textures using solid irradiance and virtual surfaces. Computers and Graphics, 23(4), 1999.Google Scholar
  5. [5]
    Reynald Dumont, Kadi Bouatouch, and Phillipe Gosselin. A progressive algorithm for three point transport. Computer Graphics Forum, 18(1):41–56, 1999.CrossRefGoogle Scholar
  6. [6]
    Steven J. Gortler, Peter Schröder, Michael F. Cohen, and Pat Hanrahan. Wavelet radiosity. In Computer Graphics Proceedings, Annual Conference Series, 1993, pages 221–230, 1993.Google Scholar
  7. [7]
    Baining Guo. Progressive radiance evaluation using directional coherence maps. In Computer Graphics (SIGGRAPH’ 98 Proceedings), pages 255–266, 1998.Google Scholar
  8. [8]
    Pat Hanrahan, David Salzman, and Larry Aupperle. A rapid hierarchical radiosity algorithm. In Computer Graphics (SIGGRAPH’ 91 Proceedings), volume 25, pages 197–206, 1991.Google Scholar
  9. [9]
    Henrik Wann Jensen and Per H. Christensen. Efficient simulation of light transport in scenes with participating media using photon maps. In SIGGRAPH’98 Conf Proceedings, pages 311–320,1998.Google Scholar
  10. [10]
    Arjan J. F. Kok and Frederik W. Jansen. Adaptive sampling of area light sources in ray tracing including diffuse interreftection. Computer Graphics Forum, 11(3):289–298, 1992.CrossRefGoogle Scholar
  11. [11]
    Daniel Lischinski, Filippo Tampieri, and Donald P. Greenberg. Combining hierarchical radiosity and discontinuity meshing. In Computer Graphics (SIGGRAPH’93 Proceedings), pages 199–208, 1993.Google Scholar
  12. [12]
    L. Mostefaoui, J.-M. Dischler, and D. Ghazanfarpur. Rendering inhomogeneous surfaces with radiosity. In Rendering Techniques’ 99 (Proc. EG Workshop on Rendering), pages 283–292. Springer, 1999.Google Scholar
  13. [13]
    James Painter and Kenneth Sloan. Antialiased ray tracing by adaptive progressive refinement. In Computer Graphics (Proc. SIGGRAPH’ 89), volume 23, pages 281–288,1989.Google Scholar
  14. [14]
    F. Perez, I. Martin, X. Pueyo, and F.X. Sillion. Acceleration of monte carlo path tracing for general environments. In Proc. Pacific Graphics 2000, 2000.Google Scholar
  15. [15]
    M. Ramasubramanian, S. Pattanaik, and D. P. Greenberg. A perceptually based physical error metric for realistic image synthesis. In Computer Graphics (SIGGRAPH’99 Proceedings), pages 73–82, 1999.Google Scholar
  16. [16]
    P. Shirley. Physically Based Lighting Calculations for Computer Graphics. PhD thesis, University of Illinois, 1991.Google Scholar
  17. [17]
    Brian Smits. Efficient Hierarchical Radiosity in Complex Environments. Ph.d thesis, Cornell University, 1994.Google Scholar
  18. [18]
    Cyril Soler and Francois X. Sillion. Fast calculation of soft shadow textures using convolution. In SIGGRAPH 98 Conference Proceedings, pages 321–332, 1998.Google Scholar
  19. [19]
    Marc Stamminger, Philipp Slusallek, and Hans-Peter Seidel. Bounded radiosity — illumination on general surfaces and clusters. Computer Graphics Forum (Eurographics’ 97), 16(3):309–318, 1997.CrossRefGoogle Scholar
  20. [20]
    W. Stürzlinger. Optimized local pass using importance sampling. In International Conference in Central Europe of Computer Graphics and Visualization’ 96, pages 342–348, 1996.Google Scholar
  21. [21]
    V. Volevich, K. Myszkowski, A. Khodulev, and E. Kopylov. Using the visible difference predictor to improve performance of progressive global illumination computation. ACM Transactions on Graphics, 19(1):122–161, April 2000.CrossRefGoogle Scholar
  22. [22]
    Andrew J. Willmott, Paul S. Heckbert, and Michael Garland. Face cluster radiosity. In Rendering Techniques 99 (Proceedings of EG Workshop on Rendering), pages 293–304. Springer, 1999.Google Scholar

Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Annette Scheel
    • 1
  • Marc Stamminger
    • 2
  • Hans-Peter Seidel
    • 1
  1. 1.Max-Planck-Institut for Computer ScienceGermany
  2. 2.iMAGIS/GRAVIR-REVES - INRIA Sophia AntipolisFrance

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