Title

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

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