Data Level Comparison of Surface Classification and Gradient Filters

  • Kwansik Kim
  • Craig M. Wittenbrink
  • Alex Pang
Conference paper
Part of the Eurographics book series (EUROGRAPH)


Surface classification and shading of three dimensional scalar data sets are important enhancements for direct volume rendering (DVR). However, unlike conventional surface rendering, DVR algorithms do not have explicit geometry to shade, making it difficult to perform comparisons. Furthermore, DVR, in general, involves a complex set of parameters whose effects on a rendered image are hard to compare. Previous work uses analytical estimations of the quality of interpolation, gradient filters, and classification. Typical comparisons are done using side-by-side examination of rendered images. However, non-linear processes are involved in the rendering pipeline and thus the comparison becomes particularly difficult. In this paper, we present a data level methodology for analyzing volume surface classification and gradient filters. Users can more effectively estimate algorithmic differences by using intermediate information. Based on this methodology, we also present new data level metrics and examples of analyzing differences in surface classification and gradient calculation. Please refer to for a full color version of this paper.


Gradient Vector Gradient Magnitude Salt Dome Surface Strength Direct Volume Rendering 
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.
    Mark J. Bentum, Barthold B.A. Lichtenbelt, and Thomas Malzbender. Frequency analysis of gradient estimators in volume rendering. IEEE Transactions on Visualization and Computer Graphics, 2 (3): 242–254, September 1996.CrossRefGoogle Scholar
  2. 2.
    R. A. Drebin, L. Carpenter, and P. Hanrahan. Volume rendering. In Proceedings of SIG-GRAPH 88, pages 65–74, August 1988.Google Scholar
  3. 3.
    Ajeetkumar Gaddipatti, Raghu Machiraju, and Roni Yagel. Steering image generation with wavelet based perceptual metric. Computer Graphics Forum, 16(3): C241–251, C391, September 1997.CrossRefGoogle Scholar
  4. 4.
    Michael E. Goss. An adjustable gradient filter for volume visualization image enhancement. In Proceedings Graphics Interface, pages 67–74, 1994.Google Scholar
  5. 5.
    S. Jin, R.S. Wu, X.B. Xie, and Z. Ma. Wave equation-based decomposition and imaging for multicomponent seismic data. Journal of Seismic Exploration, 7 (2): 145–158, 1998.Google Scholar
  6. 6.
    Kwansik Kim and Alex Pang. A methodology for comparing direct volume rendering algorithms using a projection-based data level approach. In Eurographics/IEEE TVCG Symposium on Visualization, pages 87–98, Vienna, Austria, May 1999.Google Scholar
  7. 7.
    Kwansik Kim and Alex Pang. Ray-based data level comparisons of direct volume rendering algorithms. In Hans Hagen, Gregory Nielson, and Frits Post, editors, Scientific Visualization, Dagstuhl’97 Workshop Proceedings, pages 137–150, 347. IEEE Computer Society, 1999.Google Scholar
  8. 8.
    G. Kindlmann and J.W. Durkin. Semi-automatic generation of transfer functions for direct volume rendering. In IEEE Symposium on Volume Visualization, pages 79–86, 170 IEEE, 1998.Google Scholar
  9. 9.
    Marc Levoy. Display of surfaces from volume data. IEEE Computer Graphics and Applications, 8 (5): 29–37, May 1988.CrossRefGoogle Scholar
  10. 10.
    W. Lorensen and H. Cline. Marching cubes: A high resolution 3d surface construction algorithm. Computer Graphics, 21 (4): 163–169, 1987.CrossRefGoogle Scholar
  11. 11.
    S. R. Marschner and R. J. Lobb. An evaluation of reconstruction filters for volume rendering. In Proceedings of Visualization, pages 100–107. IEEE, October 1994.Google Scholar
  12. 12.
    Torsten Möller, Raghu Machiraju, Klaus Müller, and Roni Yagel. A comparison of normal estimation schemes. In Proceedings of the IEEE Conference on Visualization 1997, pages 19–26, October 1997.Google Scholar
  13. 13.
    Torsten Möller, Klaus Müller, Yair Kurzion, Raghu Machiraju, and Roni Yagel. Design of accurate and smooth filters for function and derivative reconstruction. In Proceedings of the 1998 Symposium on Volume Visualization, pages 143–151, October 1998.CrossRefGoogle Scholar
  14. 14.
    Nivedita Sahasrabudhe, John E. West, Raghu Machiraju, and Mark Janus. Structured spatial domain image and data comparison metrics. In Proceedings of Visualization 99, pages 97–104,515, 1999.Google Scholar
  15. 15.
    Peter L. Williams and Samuel P. Uselton. Metrics and generation specifications for comparing volume-rendered images. The Journal of Visualization and Computer Animation, 10: 159–178, 1999.CrossRefGoogle Scholar
  16. 16.
    Craig M. Wittenbrink, Thomas Malzbender, and Michael E. Goss. Opacity-weighted color interpolation for volume sampling. In Proceedings of the 1998 Symposium on Volume Visualization, pages 135–142, color plate page 177, October 1998. Also available as Technical Report, HPL-97–31R2, Hewlett-Packard Laboratories, Palo Alto, CA, Revised Apr. 1998.Google Scholar

Copyright information

© Springer-Verlag/Wien 2001

Authors and Affiliations

  • Kwansik Kim
    • 1
  • Craig M. Wittenbrink
    • 2
  • Alex Pang
    • 1
  1. 1.Computer Science DepartmentUniversity of CaliforniaSanta CruzUSA
  2. 2.Hewlett-Packard LaboratoriesPalo AltoUSA

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