Visibility Culling Using Plenoptic Opacity Functions for Large Data Visualization
Document Type
Conference Presentation
Department
Computer Science
Conference Title
IEEE Visualization
Location
Seattle, WA
Conference Dates
October 19-24, 2003
Date of Presentation
10-19-2003
Abstract
Visibility culling has the potential to accelerate large data visualization in significant ways. Unfortunately, existing algorithms do not scale well when parallelized, and require full re-computation whenever the opacity transfer function is modified. To address these issues, we have designed a Plenoptic Opacity Function (POF) scheme to encode the view-dependent opacity of a volume block. POFs are computed off-line during a pre-processing stage, only once for each block. We show that using POFs is (i) an efficient, conservative and effective way to encode the opacity variations of a volume block for a range of views, (ii) flexible for re-use by a family of opacity transfer functions without the need for additional off-line processing, and (iii) highly scalable for use in massively parallel implementations. Our results confirm the efficacy of POFs for visibility culling in large-scale parallel volume rendering; we can interactively render the Visible Woman dataset using software ray-casting on 32 processors, with interactive modification of the opacity transfer function on-the-fly.
First Page
341
Last Page
348
DOI
10.1109/VISUAL.2003.1250391
Recommended Citation
Gao, J.,
Huang, J.,
Shen, H.,
&
Kohl, J.
(2003).
Visibility Culling Using Plenoptic Opacity Functions for Large Data Visualization.
Paper presented at IEEE Visualization in Seattle, WA.
https://scholarlycommons.pacific.edu/soecs-facpres/88
