An Isosurface Continuity Algorithm for Super Adaptive Resolution Data *
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
KeywordsCoarse Resolution Adaptive Mesh Refinement Volume Visualization Marching Cube Algorithm Triangle Vertex
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- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Renben Shu, Chen Zhou, and Mohan S Kankanhalli. Adaptive marching cubes. The Visual Computer,11:202–217, 1995.Google Scholar
- 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
- 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