Visibility Culling Using Plenoptic Opacity Functions for Large Volume Visualization

Jinzhu Gao
OSU Graduate Student
http://www.cse.ohio-state.edu/~hwshen/Research/index.html

Weds., Oct. 8th
11:30am; 480 Dreese Labs
All interested parties are invited.
Pizza lunch will be served.

Abstract:
Visibility culling has the potential to accelerate large data visualiza-tion in significant ways.Unfortunately, existing algorithms do not scale well when parallelized, and require full re-computation when-ever 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 process-ing, and (iii) highly scalable for use in massively parallel imple-mentations. 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.