SPACE EFFICIENT FAST ISOSURFACE EXTRACTION
FOR LARGE DATASETS

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

Weds., Oct. 15th
11:30 480 Dreese Labs
All interested parties are invited.
Pizza lunch will be served.

Abstract:
In this talk, we will present a space efficient algorithm for speeding
up isosurface extraction. Even though there exist algorithms that can
achieve optimal search performance to identify isosurface cells, they
prove impractical for large datasets due to a high storage overhead.
With the dual goals of achieving fast isosurface extraction and
simultaneously reducing the space requirement, we introduce an algorithm
based on transform coding to compress the interval information of the
cells in a dataset. Compression is achieved by first transforming the
cell intervals (minima, maxima) into a form which allows more efficient
compaction. It is followed by a dataset optimized non-uniform
quantization stage. The compressed data is stored in a data structure
that allows fast searches in the compression domain, thus eliminating
the need to retrieve the original representation of intervals at
run-time. The space requirement of our search data structure is the
mandatory cost of storing every cell id once, plus an overhead for
quantization information. The overhead is typically in the order of a
few hundredths of the dataset size.

This talk is based on an IEEE Visualization 2003 publication authored by
Udeepta Bordoloi and Dr.Han-Wei Shen.