Descriptions and evaluations of methods for determining surface curvature in volumetric data. (February 2020)

• Record Type: Journal Article
• Authors: Hauenstein, Jacob D.; Newman, Timothy S.
• Is Part Of: Computers & graphics. Volume 86(2020)
• Page Start: 52
• Page End: 70
• Publication Date: 2020-02
• Subjects: Curvature -- Implicit surfaces -- Volume data -- Direct volume rendering; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2019.11.003
• Languages: English
• ISSNs: 0097-8493

Abstract

Highlights: Methods using convolution or fitting are often the most accurate. The existing TE method is fast and accurate on noise-free data. The OP method is faster than existing, similarly accurate methods on real data. Even modest errors in curvature notably impact curvature-based renderings. On real data, GSTH, GSTI, and OP produce the best curvature-based renderings. Graphical abstract: Abstract: Three methods developed for determining surface curvature in volumetric data are described, including one convolution-based method, one fitting-based method, and one method that uses normal estimates to directly determine curvature. Additionally, a study of the accuracy and computational performance of these methods and prior methods is presented. The study considers synthetic data, noise-added synthetic data, and real data. Sample volume renderings using curvature-based transfer functions, where curvatures were determined with the methods, are also exhibited.