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VOTS: VOlume doTS as a Point-Based Representation of Volumetric Data

Sören Grimm, Stefan Bruckner, Armin Kanitsar, M. Eduard Gröller

JOURNAL ARTICLE: Computer Graphics Forum, vol. 23, no. 3, pp. 668–661, 2004. DOI: 10.1111/j.1467-8659..00798.x

Abstract

We present Volume dots (Vots), a new primitive for volumetric data modelling, processing, and rendering. Vots are a point-based representation of volumetric data. An individual Vot is specified by the coefficients of a Taylor series expansion, i.e. the function value and higher order derivatives at a specific point. A Vot does not only represent a single sample point, it represents the underlying function within a region. With the Vots representation we have a more intuitive and high-level description of the volume data. This allows direct analytical examination and manipulation of volumetric datasets. Vots enable the representation of the underlying scalar function with specified precision. User-centric importance sampling is also possible, i.e., unimportant volume parts are still present but represented with just very few Vots. As proof of concept, we show Maximum Intensity Projection based on Vots.

Published

Computer Graphics Forum

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BibTeX

@ARTICLE{Grimm-2004-VVD,
  author = {S{\"o}ren Grimm and Stefan Bruckner and Armin Kanitsar and M.
	Eduard Gr{\"o}ller},
  title = {VOTS: VOlume doTS as a Point-Based Representation of Volumetric Data},
  journal = {Computer Graphics Forum},
  year = {2004},
  volume = {23},
  pages = {668--661},
  number = {3},
  month = sep,
  abstract = {We present Volume dots (Vots), a new primitive for volumetric data
	modelling, processing, and rendering. Vots are a point-based representation
	of volumetric data. An individual Vot is specified by the coefficients
	of a Taylor series expansion, i.e. the function value and higher
	order derivatives at a specific point. A Vot does not only represent
	a single sample point, it represents the underlying function within
	a region. With the Vots representation we have a more intuitive and
	high-level description of the volume data. This allows direct analytical
	examination and manipulation of volumetric datasets. Vots enable
	the representation of the underlying scalar function with specified
	precision. User-centric importance sampling is also possible, i.e.,
	unimportant volume parts are still present but represented with just
	very few Vots. As proof of concept, we show Maximum Intensity Projection
	based on Vots.},
  doi = {10.1111/j.1467-8659..00798.x},
  issn = {0167-7055},
  keywords = {point-based data, volume data},
  url = {http://www.cg.tuwien.ac.at/research/publications/2004/grimm-2004-volume/}
}






 Last Modified: Stefan Bruckner, 2014-08-15