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Performance and Quality Analysis of Convolution-Based Volume Illumination

Paolo Angelelli, Stefan Bruckner

JOURNAL ARTICLE: Journal of WSCG, vol. 23, no. 2, pp. 131–138, 2015.

Abstract

Convolution-based techniques for volume rendering are among the fastest in the on-the-fly volumetric illumination category. Such methods, however, are still considerably slower than conventional local illumination techniques. In this paper we describe how to adapt two commonly used strategies for reducing aliasing artifacts, namely pre-integration and supersampling, to such techniques. These strategies can help reduce the sampling rate of the lighting information (thus the number of convolutions), bringing considerable performance benefits. We present a comparative analysis of their effectiveness in offering performance improvements. We also analyze the (negligible) differences they introduce when comparing their output to the reference method. These strategies can be highly beneficial in setups where direct volume rendering of continuously streaming data is desired and continuous recomputation of full lighting information is too expensive, or where memory constraints make it preferable not to keep additional precomputed volumetric data in memory. In such situations these strategies make single pass, convolution-based volumetric illumination models viable for a broader range of applications, and this paper provides practical guidelines for using and tuning such strategies to specific use cases.

Published

Journal of WSCG

  • Volume: 23
  • Number: 2
  • Pages: 131–138
  • Date: June 2015

Documents and Links

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BibTeX

@ARTICLE{Angelelli-2015-PQA,
  author = {Paolo Angelelli and Stefan Bruckner},
  title = {Performance and Quality Analysis of Convolution-Based Volume Illumination},
  journal = {Journal of WSCG},
  year = {2015},
  volume = {23},
  pages = {131--138},
  number = {2},
  month = jun,
  abstract = {Convolution-based techniques for volume rendering are among the fastest
	in the on-the-fly volumetric illumination category. Such methods,
	however, are still considerably slower than conventional local illumination
	techniques. In this paper we describe how to adapt two commonly used
	strategies for reducing aliasing artifacts, namely pre-integration
	and supersampling, to such techniques. These strategies can help
	reduce the sampling rate of the lighting information (thus the number
	of convolutions), bringing considerable performance benefits. We
	performance improvements. We also analyze the (negligible) differences
	they introduce when comparing their output to the reference method.
	These strategies can be highly beneficial in setups where direct
	volume rendering of continuously streaming data is desired and continuous
	recomputation of full lighting information is too expensive, or where
	memory constraints make it preferable not to keep additional precomputed
	volumetric data in memory. In such situations these strategies make
	single pass, convolution-based volumetric illumination models viable
	for a broader range of applications, and this paper provides practical
	guidelines for using and tuning such strategies to specific use cases.},
  keywords = {volume rendering, global illumination, scientific visualization, medical
	visualization},
}






 Last Modified: Stefan Bruckner, 2017-06-14