Visibility Culling for Time-Varying Volume Rendering Using Temporal Occlusion Coherence
Document Type
Conference Presentation
Department
Computer Science
Conference Title
IEEE Visualization
Location
Austin, TX
Conference Dates
October 10-15, 2004
Date of Presentation
10-10-2004
Abstract
Typically there is a high coherence in data values between neighboring time steps in an iterative scientific software simulation; this characteristic similarly contributes to a corresponding coherence in the visibility of volume blocks when these consecutive time steps are rendered. Yet traditional visibility culling algorithms were mainly designed for static data, without consideration of such potential temporal coherency. We explore the use of temporal occlusion coherence (TOC) to accelerate visibility culling for time-varying volume rendering. In our algorithm, the opacity of volume blocks is encoded by means of plenoptic opacity functions (POFs). A coherence-based block fusion technique is employed to coalesce time-coherent data blocks over a span of time steps into a single, representative block. Then POFs need only be computed for these representative blocks. To quickly determine the subvolumes that do not require updates in their visibility status for each subsequent time step, a hierarchical "TOC tree" data structure is constructed to store the spans of coherent time steps. To achieve maximal culling potential, while remaining conservative, we have extended our previous POP into an optimized POP (OPOP) encoding scheme for this specific scenario. To test our general TOC and OPOF approach, we have designed a parallel time-varying volume rendering algorithm accelerated by visibility culling. Results from experimental runs on a 32-processor cluster confirm both the effectiveness and scalability of our approach.
First Page
147
Last Page
154
DOI
10.1109/VISUAL.2004.110
Recommended Citation
Gao, J.,
Huang, J.,
Shen, H.,
&
Kohl, J.
(2004).
Visibility Culling for Time-Varying Volume Rendering Using Temporal Occlusion Coherence.
Paper presented at IEEE Visualization in Austin, TX.
https://scholarlycommons.pacific.edu/soecs-facpres/87