Descriptions and evaluations of methods for determining surface curvature in volumetric data

•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,...

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Bibliographic Details
Published in:Computers & graphics 2020-02, Vol.86, p.52-70
Main Authors: Hauenstein, Jacob D., Newman, Timothy S.
Format: Article
Language:English
Subjects:
Convolution
Curvature
Direct volume rendering
Implicit surfaces
Methods
Transfer functions
Volume data
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Summary:•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. [Display omitted] 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.
ISSN:0097-8493
1873-7684
DOI:10.1016/j.cag.2019.11.003