Title:	Performance and quality analysis of convolution-based volume illumination
Authors:	Angelelli, Paolo Bruckner, Stefan
Citation:	Journal of WSCG. 2015, vol. 23, no. 1, p. 131-138.
Issue Date:	2015
Publisher:	Václav Skala - UNION Agency
Document type:	článek article
URI:	http://wscg.zcu.cz/WSCG2015/!_2015_Journal_WSCG-No-2.pdf http://hdl.handle.net/11025/17150
ISSN:	1213–6972 (hardcopy) 1213–6980 (CD-ROM) 1213–6964 (online)
Keywords:	objemové vykreslování;globální osvětlení;vědecká vizualizace;lékařská vizualizace
Keywords in different language:	volume rendering;global illumination;scientific visualization;medical visualization
Abstract in different language:	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.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	Volume 23, Number 2 (2015)

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