Visibility Culling Using Plenoptic Opacity Functions for Large Volume Visualization

  • Authors:
  • Jinzhu Gao;Jian Huang;Han-Wei Shen;James Arthur Kohl

  • Affiliations:
  • The Ohio State Univ.;The Univ. of Tennessee;The Ohio State Univ.;Oak Ridge National Lab

  • Venue:
  • Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

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.