Project 3

View Dependent Algorithms

Abstract

Dynamic View Selection for Time-varying Volumes

IEEE Visualization 2006, also in a special issue of IEEE Transactions on Visualization and Computer Graphics

Guangfeng Ji and Han-Wei Shen

View Selection for Volume Rendernig‭

‬IEEE Visualization

Udeepta Bordoloi and Han-Wei Shen‭, 2005‭

Visibility Culling for Time-Varying Volume Rendernig

Using Temporal Occlusion Coherence‭

IEEE Visualization 2004

Jinzhu Gao‭, ‬Han-Wei Shen‭, ‬Jian Huang‭, ‬and Jim Kohl‭‭, ‬October 2004‭‬

Visibility Culling Using Plenoptic Opacity Function

for Large Scale Data Visualization‭

‬IEEE Visualization 2003‭

Jinzhu Gao‭, ‬Jian Huang‭, ‬Han-Wei Shen‭, ‬and Jim Kohl‭, ‬October 2003‭

Hardware-Assisted View-Dependent Isosurface Extraction

using Spherical Partition‭

oint Eurographics-IEEE TCVG Symposium on Visualization‭

Jinzhu Gao‭ ‬and Han-Wei Shen, ‬May 2003

Publication

Due to the sheer size of data, it is often effective to optimize the visualization algorithms in a view-dependent manner. For example, visibility culling can be used to reduce the underlying data complexity and accelerate rendering speed. In addition, when it is expensive to visualize the data from arbitrary iviews at interactive speeds, there is a need to consider finding the optimial views so that the user can more quickly glean insight into the data with just a few representative visualization images.

One focus of the Gravite Research Group is to develop efficient view dependent algorithms for large scale volume data. To date, we have developed algorithms to perform efficient visibility

culling algorithms for static and time-varying volume data. We have also developed effective view selection algorithms to volume rendering.

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