An Isosurface Continuity Algorithm for Super Adaptive Resolution Data *

  • Robert S Laramee
  • R. Daniel Bergeron


We present the chain-gang algorithm for isosurface rendering of super adap tive resolution (SAR) volume data in order to minimize (1) the space needed for storage of both the data and the isosurface and (2) the time taken for computation. The chain gang algorithm is able to resolve discontinuities in SAR data sets. Unnecessary computation is avoided by skipping over large sets of volume data deemed uninteresting. Memory space is saved by leaving the uninteresting voxels out of our octree data structure used to traverse the volume data. Our isosurface generation algorithm extends the Marching Cubes Algorithm in order to handle inconsistencies that can arise between abutting cells that are separated by both one and two levels of resolution.


isosurface rendering, adaptive resolution visualization, marching cubes, uncertainty visualization, chain-gang


Coarse Resolution Adaptive Mesh Refinement Volume Visualization Marching Cube Algorithm Triangle Vertex 
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]
    Michael B. Cox and David Ellsworth. Application-controlled demand paging for out-of-core visualization. Proc. Of Proc ‘87. IEEE Computer Society Press, October 1997.Google Scholar
  2. [2]
    Michael B. Cox and David Ellsworth. Managing big data for scientific visualization. ACM Siggraph ‘87, 21, August 1997. Course #4 Exploring Gigabyte Datasets in Real-Time: Algorithms, Data Management, and Time-Critical Design.Google Scholar
  3. [3]
    Klaus Engel, Rudiger Westermann, and Thomas Ertl. Isosurface extraction techniques for web-based volume visualization. In Volume Visualization,Visualization 99, California, October 1999.Google Scholar
  4. [4]
    William Hibbard. Connecting people to computations and people to people.Computer Graphics,32(3):10–12,1998.CrossRefGoogle Scholar
  5. [5]
    William Hibbard. The visad java class library developers guide. The World Wide Web, November 1999. http://www.ssec.wisc.edulbillh/visad.html Google Scholar
  6. [6]
    Eric C. LaMar, Bernd Hamann, and Kenneth I. Joy. Multiresolution techniques for interactive texture-based volume visualization. In David Ebert, Markus Gross, and Bernd Hamann, editors, IEEE Visualization ‘89,pages 355–362, San Francisco, 1999. IEEE.Google Scholar
  7. [7]
    C. Charles Law, Kenneth M. Martin, William J. Schroeder, and Joshua Temkin. A multi-threaded streaming pipeline architecture for large structured data sets. In Volume Visualization,Volume Visualization 99, California, October 1999.Google Scholar
  8. [8]
    Yarden Livnat, Han-Wei Shen, and Christopher R. Johnson. A near optimal isosurface extraction algorithm for structured and unstructured grids. IEEE Transactions on Visual Computer Graphics,2(1):73–84,1996.CrossRefGoogle Scholar
  9. [9]
    William E. Lorensen and Harvey E. Cline. Marching cubes: a high resolution 3d surface construction algorithm. Comput Graph,21:163–169, 1987.CrossRefGoogle Scholar
  10. [10]
    Claudio Montani, Riccardo Scateni, and Roberto Scopigno. Discretized marching cubes. In R. Daniel Bergeron and Arie E. Kaufman, editors, Proceedings of the Conference on Visualization,pages 281–287, Los Alamitos, CA, USA, October 1994. IEEE Computer Society Press.Google Scholar
  11. [11]
    Alex T. Pang, Craig M. Wittenbrink, and Suresh K. Lodha. Approaches to uncertainty visualization. The Visual Computer,13(8):370–390, 1997. ISSN 01782789.CrossRefGoogle Scholar
  12. [12]
    William J. Schroeder, Kenneth M. Martin, and William E. Lorensen. The Visualization Toolkit. Prentice-Hall, Inc, Upper Saddle River, New Jersey 07458, 1996.Google Scholar
  13. [13]
    Raj Shekhar, Elias Fayyad, Roni Yagel, and J. Fredrick Cornhill. Octree-based decimation of marching cubes surfaces. In Roni Yagel and Gregory M. Nielson, editors, Pmceedings of the Conference on Visualization,pages 335–344, Los Alamitos, October 27—November 1 1996. IEEE.Google Scholar
  14. [14]
    Renben Shu, Chen Zhou, and Mohan S Kankanhalli. Adaptive marching cubes. The Visual Computer,11:202–217, 1995.Google Scholar
  15. [15]
    Gunther H. Weber, Oliver Kreylos, Terry J Ligocki, John M. Shalf, Hans Hagen, Bernd Hamann, and Kenneth I Joy. Extraction of crack-free isosurfaces from adaptive mesh refinement data. In D.S. Ebert, J.M. Favre, and R. Peikert, editors,Data Visualization 2001(Proceedings of VisSym 2001),pages 25–34, Vienna, Austria, 2001. Springer-Verlag.Google Scholar
  16. [16]
    Ruediger Westermann, Leif P. Kobbelt, and Thomas Ertl. Real-time exploration of regular volume data by adaptive reconstruction of isosurfaces. The Visual Computer, 15:100–111,1999.CrossRefGoogle Scholar
  17. [17]
    Jane Wilhelms and Allen Van Gelder. Topological ambiguities in isosurface generation. Technical report, University of California, Santa Cruz, California, December 1990. Extended abstract in ACM Computer Graphics. 2, 5 79–86.CrossRefGoogle Scholar
  18. [18]
    Jane Wilhelms and Allen Van Gelder. Octrees for faster isosurface generation. ACM Transactions on Graphics,11(3):201–227, July 1992.MATHCrossRefGoogle Scholar
  19. [19]
    Pak Chung Wong and R. Daniel Bergeron. Multiresolution multidimensional wavelet brushing. In Roni Yagel and Gregory M. Nielson, editors, IEEE Visualization ‘86, pages 141–148. IEEE, 1996.Google Scholar

Copyright information

© Springer-Verlag London 2002

Authors and Affiliations

  • Robert S Laramee
    • 1
  • R. Daniel Bergeron
    • 2
  1. 1.Center for Virtual Reality and Visualization Donau-City-StrassViennaAustria
  2. 2.Department of Computer Science Kingsbury HallUniversity of New Hampshire DurhamUSA

Personalised recommendations