Research Visualization Edge Groups: A New Approach to Understanding the Mesh Quality of Marching Methods
Scientific Visualization
Scientific visualization, sometimes referred to as visual data analysis, uses the graphical representation of data as a means of gaining understanding and insight into the data. Scientific visualization research at SCI has focused on applications spanning computational fluid dynamics, medical imaging and analysis, and fire simulations. Research involves novel algorithm development to building tools and systems that assist in the comprehension of massive amounts of scientific data. In helping researchers to comprehend spatial and temporal relationships between data, interactive techniques provide better cues than noninteractive techniques; therefore, much of scientific visualization research focuses on better methods for visualization and rendering at interactive rates.
| Edge Groups: A New Approach to Understanding the Mesh Quality of Marching Methods |
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Marching Cubes (MC) is the most popular isosurface extraction algorithm due to its simplicity, efficiency and robustness and has been widely studied, improved, and extended. As part of study to improve MC results for applications in scientific computing, we have developed a new classification scheme called “Edge Groups”, which helps improve the quality of resulting surfaces. This formulation allows for a more systematic way to control the quality of triangles that make up the surface and is general enough to extend to other polyhedral cell shapes. |
