The Community for Technology Leaders
Visualization Symposium, IEEE Pacific (2012)
Songdo, Korea (South)
Feb. 28, 2012 to Mar. 2, 2012
ISBN: 978-1-4673-0863-2
pp: 161-168
Natallia Kotava , University of Kaiserslautern, Germany
Aaron Knoll , Argonne National Laboratory, USA
Mathias Schott , University of Utah, USA
Christoph Garth , University of Kaiserslautern, Germany
Xavier Tricoche , Purdue University, USA
Christoph Kessler , University of Kaiserslautern, Germany
Elaine Cohen , University of Utah, USA
Charles D. Hansen , University of Utah, USA
Michael E. Papka , Argonne National Laboratory, USA
Hans Hagen , University of Kaiserslautern, Germany
Peak finding provides more accurate classification for direct volume rendering by sampling directly at local maxima in a transfer function, allowing for better reproduction of high-frequency features. However, the 1D peak finding technique does not extend to higherdimensional classification. In this work, we develop a new method for peak finding with multidimensional transfer functions, which looks for peaks along the image of the ray. We use piecewise approximations to dynamically sample in transfer function space between world-space samples. As with unidimensional peak finding, this approach is useful for specifying transfer functions with greater precision, and for accurately rendering noisy volume data at lower sampling rates. Multidimensional peak finding produces comparable image quality with order-of-magnitude better performance, and can reproduce features omitted entirely by standard classification. With no precomputation or storage requirements, it is an attractive alternative to preintegration for multidimensional transfer functions.

X. Tricoche et al., "Volume rendering with multidimensional peak finding," Visualization Symposium, IEEE Pacific(PACIFICVIS), Songdo, Korea (South), 2012, pp. 161-168.
186 ms
(Ver 3.3 (11022016))