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The Institute for Data Analysis and Visualization (IDAV) consists of seven faculty members and approximately 30 researchers and graduate students all working on interdisciplinary problems in visualization, geometric modeling, computer graphics, computational geometry, graphics architecture, and immersive technologies. We are key partners in VACET, a DoE SciDAC II Visualization and Analytics Center for Enabling Technologies, and in the IRTG, an International Research Training Group based at the Technical University of Kaiserslautern in Germany. We are an internationally recognized leader in the fields of visualization and computer graphics, addressing problems that range from the multiresolution display of very large-scale data, to the discovery of new visual analysis algorithms, to the design of methods to accelerate the use of graphics hardware. Featured Project
Accurate and Efficient Integral Surfaces in Time-Dependent Vector Fields
Integral surfaces, as a natural generalization of integral lines, are a basic yet powerful tool for insightful vector field visualization. Representing a continuum of integral lines, they constitute a surface and allow for the application of surface shading techniques. In comparison to streamlines or other line-based techniques, they support depth perception and greatly facilitate the visual understanding of complex three-dimensional ... [more] |
Latest News July 15, 2008
IDAV researchers win an "OASCR" at the 2008 SciDAC Conference
June 1, 2008
Shubho Sengupta chosen as the outstanding graduate student in Computer Science
November 1, 2007
Two IDAV research papers nominated for best paper at IEEE Visualization 2007
Recent Publications
Kazhdan,
Amenta,
Gu,
Wiley,
Hamann,
"Symmetry restoration by stretching",
in Canadian Conference on Computational Geometry,
2009
Ghosh,
Sharf,
Amenta,
"Feature-driven deformation for dense correspondence",
in Proceedings of SPIE Medical Imaging,
Vol 7261,
pp 36,
2009
Krishnan,
Garth,
Joy,
"Time and Streak Surfaces for Flow Visualization in Large Time-Varying Data Sets",
in Proceedings of IEEE Visualization '09,
2009
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