By Topic

Extended specifications and test data sets for data level comparisons of direct volume rendering algorithms

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Kwansik Kim ; Unigraphics Div., Electron. Data Syst. Corp., Cypress, CA, USA ; Wittenbrink, C.M. ; Pang, A.

Direct volume rendering (DVR) algorithms do not generate intermediate geometry to create a visualization, yet they produce countless variations in the resulting images. Therefore, comparative studies are essential for objective interpretation. Even though image and data level comparison metrics are available, it is still difficult to compare results because of the numerous rendering parameters and algorithm specifications involved. Most of the previous comparison methods use information from the final rendered images only. We overcome limitations of image level comparisons with our data level approach using intermediate rendering information. We provide a list of rendering parameters and algorithm specifications to guide comparison studies. We extend Williams and Uselton's rendering parameter list with algorithm specification items and provide guidance on how to compare algorithms. Real data are often too complex to study algorithm variations with confidence. Most of the analytic test data sets reported are often useful only for a limited feature of DVR algorithms. We provide simple and easily reproducible test data sets, a checkerboard and a ramp, that can make clear differences in a wide range of algorithm variations. With data level metrics, our test data sets make it possible to perform detailed comparison studies. A number of examples illustrate how to use these tools

Published in:

Visualization and Computer Graphics, IEEE Transactions on  (Volume:7 ,  Issue: 4 )