Perceptually Driven Simplification for Interactive Rendering

  • David Luebke
  • Benjamin Hallen
Part of the Eurographics book series (EUROGRAPH)


We present a framework for accelerating interactive rendering, grounded in psychophysical models of visual perception. This framework is applicable to multiresolution rendering techniques that use a hierarchy of local simplification operations. Our method drives those local operations directly by perceptual metrics; the effect of each simplification on the final image is considered in terms of the contrast the operation will induce in the image and the spatial frequency of the resulting change. A simple and conservative perceptual model determines under what conditions the simplification operation will be perceptible, enabling imperceptible simplification in which operations are performed only when judged imperceptible. Alternatively, simplifications may be ordered according to their perceptibility, providing a principled approach to best-effort rendering. We demonstrate this framework applied to view-dependent polygonal simplification. Our approach addresses many interesting topics in the acceleration of interactive rendering, including imperceptible simplification, silhouette preservation, and gaze-directed rendering.


Spatial Frequency Computer Graphic Contrast Sensitivity Threshold Contrast Perceptual Model 
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]
    Barten, Peter. “The Square-Root Integral”, In Human Vision, Visual Processing, and Digital Display, vol. 1077, Proceedings SPIE (1989)Google Scholar
  2. [2]
    Bolin, Mark. and G. Meyer. “A Perceptually Based Adaptive Sampling Algorithm”, Computer Graphics, Vol. 32 (SIGGRAPH 98).Google Scholar
  3. [3]
    Campbell, F., Gubisch, R. “Optical Quality ofthe Human Eye”, Journal of Physiology 186.Google Scholar
  4. [4]
    Campbell, F.W. and Robson, J.G. “An Application of Fourier Analysis to the Visibility of Contrast Gratings”, Journal of Physiology, 187 (1968)Google Scholar
  5. [5]
    Clark, James H. “Hierarchical Geometric Models for Visible Surface Algorithms,” Communications of the ACM, Vol. 19, No 10, pp 547–554.Google Scholar
  6. [6]
    Cohen, J, M. Olano, and D. Manocha. “Appearance-Preserving Simplification,” Computer Graphics, Vol. 32 (SIGGRAPH 98).Google Scholar
  7. [7]
    Daly, S. “Visible differences predictor: An algorithm for the assessment of image fidelity,” Digital Images and Human Vision (A. Watson, ed.), pp 179–206, MIT Press (1993).Google Scholar
  8. [8]
    Ferdwada, James, S. Pattanaik, P. Shirley, and D. Greenberg. “A Model of Visual Masking for Realistic Image Synthesis”, Computer Graphics, Vol. 30 (SIGGRAPH 96).Google Scholar
  9. [9]
    Funkhouser, Tom, and C. Sequin. “Adaptive display algorithm for interactive frame rates during visualization of complex virtual environments”, Computer Graphics, Vol. 27.Google Scholar
  10. [10]
    Heckbert, Paul, and M. Garland. “Survey of Polygonal Surface Simplification Algorithms”, SIGGRAPH 97 course notes (1997).Google Scholar
  11. [11]
    Hoppe, Hughes. “View-Dependent Refinement of Progressive Meshes”, Computer Graphics, Vol. 31 (SIGGRAPH 97).Google Scholar
  12. [12]
    Johnson, David, and E. Cohen. “Spatialized Normal Cone Hierarchies”, Proceedings ACM Symposium on Interactive 3D Graphics (2001).Google Scholar
  13. [13]
    Kelly, D.H. “Spatial Frequency Selectivity in the Retina”, Vision Research, 15 (1975).Google Scholar
  14. [14]
    Lindstrom, Peter. and Turk, G. “Image-Based Simplification”, ACM Transactions on Graphics, July 2000 (2000).Google Scholar
  15. [15]
    Lubin, Jeffery. “A Visual Discrimination Model for Imaging System Design and Evaluation”, Vision Modelsfor Target Detection and Recognition, World Scientific (1995).Google Scholar
  16. [16]
    Luebke, David, and C. Erikson. “View-Dependent Simplification of Arbitrary Polygonal Environments”, Computer Graphics, Vol. 31 (SIGGRAPH 97).Google Scholar
  17. [17]
    Luebke, David. “A Developer’s Survey of Polygonal Simplification Algorithms”, IEEE Computer Graphics & Applications (May 2001). See tech report CS-99-07, U of Virginia.Google Scholar
  18. [18]
    Luebke, David, and B. Hallen. “Perceptually-Driven Interactive Rendering”. Technical report CS-2OO1-01, University of Virginia (2000).Google Scholar
  19. [19]
    Luebke, David. See Scholar
  20. [20]
    J. L. Mannos, D. J. Sakrison, “The Effects of a Visual Fidelity Criterion on the Encoding of Images”, IEEE Transactions on Information Theory, pp. 525–535, Vol. 20, No 4, (1974).MATHCrossRefGoogle Scholar
  21. [21]
    Oshima, Toshikazu, H. Yamammoto, and H. Tamura. “Gaze-Directed Adaptive Rendering for Interacting with Virtual Space”, Proceedings of VRAIS 96 (1996).Google Scholar
  22. [22]
    Puppo, Enrico, and R. Scopigno. “Simplification, LOD and Mu1tiresolution—Principles and Applications”, Eurographics’ 97 Tutorial Notes, PS97 TN4 (1997).Google Scholar
  23. [23]
    Ramasubramanian, Mahesh, S. Pattanaik, and D. Greenberg. “A Perceptually Based Physical Error Metric for Realistic Image Synthesis”, Computer Graphics, Vol. 33.Google Scholar
  24. [24]
    Reddy, Martin. “Perceptually-Modulated Level of Detail for Virtual Environments”, Ph.D. thesis, University of Edinburgh, 1997.Google Scholar
  25. [25]
    Rovamo, J. and Virsu, V. “An Estimation and Application of the Human Cortical Magnification Factor”, Experimental Brain Research, 37 (1979)Google Scholar
  26. [26]
    Rushmeier, H., G. Ward, C. Piatko, P. Sanders, and B. Rust. “Comparing Real and Synthetic Images: Some Ideas About Metrics,” In Rendering Techniques’ 95, pp 82–91, Springer-Verlag (1995).Google Scholar
  27. [27]
    Rusinkiewicz, S. and Levoy, M. “QSplat: A Multiresolution Point Rendering System for Large Meshes”, Computer Graphics, Vol. 34 (SIGGRAPH 2000).Google Scholar
  28. [28]
    Scoggins, Randy, R. Machiraju, and R. Moorhead. “Enabling Level-of-Detail Matching for Exterior Scene Synthesis”, Proceedings of IEEE Visualization 2000 (2000).Google Scholar
  29. [29]
    Walter, Bruce, P. M. Hubbard, P. Shirley, and D. Greenberg. “Global Illumination using Local Linear Density Estimation”, ACM Transaction on Graphics (1997).Google Scholar
  30. [30]
    Xia, Julie and Amitabh Varshney. “Dynamic View-Dependent Simplification for Polygonal Models”, Visualization 96.Google Scholar
  31. [31]
    Zhang, Hansong, and K. Hoff. “Fast Backface Culling Using Normal Masks”, Proceedings of ACM Symposium on Interactive 3D Graphics (1997).Google Scholar

Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • David Luebke
    • 1
  • Benjamin Hallen
    • 1
  1. 1.University of VirginiaUSA

Personalised recommendations