seeVisible - Parallel Visibility Culling for Large Data Visualization
As simulations detailing unprecedented spatial and temporal
scales become prevalent in today's scientific research,
large data visualization, as a research agenda, has attained
a high priority. Herein, one of the primary challenges in large
data visualization stems from the
forever widening gap between the size of current data sets,
commonly amounting to hundreds of gigabytes to terabytes,
verus the sustainable system bandwidth available to
visualization algorithms. Our past and current efforts on
the subject of large data visualization include both
algorithm research on view-dependent data culling, and
systems research on developing parallel distributed systems.
We have already published the following papers on the
'Distributed Data Management for Large Volume Visualization',
J. Gao, J. Huang, C. R. Johnson, S. Atchley, J. Kohl,
Proc. of IEEE Visualization Conference, Minneapolis, MN, October, 2005.
'Visibility Culling for Time-Varying Volume Rendering Using
Temporal Occlusion Coherence', J. Gao, H. Shen, J. Huang, J. Kohl,
Proc. of IEEE Visualization Conference, Austin, TX, October, 2004.
'Visibility Culling Using Plenoptic Opacity Functions for Large
Data Visualization', J. Gao, J. Huang, H. Shen, J. Kohl,
Proc. of IEEE Visualization Conference, Seattle, October, 2003.