Rendering of feature-rich dynamically changing volumetric datasets on GPU

Martin Schreiber, Atanas Atanasov, Philipp Neumann, Hans Joachim Bungartz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Interactive photo-realistic representation of dynamic liquid volumes is a challenging task for today's GPUs and state-of-the-art visualization algorithms. Methods of the last two decades consider either static volumetric datasets applying several optimizations for volume casting, or dynamic volumetric datasets with rough approximations to realistic rendering. Nevertheless, accurate real-time visualization of dynamic datasets is crucial in areas of scientific visualization as well as areas demanding for accurate rendering of feature-rich datasets. An accurate and thus realistic visualization of such datasets leads to new challenges: due to restrictions given by computational performance, the datasets may be relatively small compared to the screen resolution, and thus each voxel has to be rendered highly oversampled. With our volumetric datasets based on a real-time lattice Boltzmann fluid simulation creating dynamic cavities and small droplets, existing real-time implementations are not applicable for a realistic surface extraction. This work presents a volume tracing algorithm capable of producing multiple refractions which is also robust to small droplets and cavities. Furthermore we show advantages of our volume tracing algorithm compared to other implementations.
Original languageEnglish (US)
Title of host publicationProcedia Computer Science
PublisherElsevier BV
Number of pages11
StatePublished - Jun 6 2014
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-24
Acknowledged KAUST grant number(s): UK-c0020
Acknowledgements: With respect to the efficiency of our approach on the current GPUs, the results show that the DDA-based volume tracing algorithm is competitive to alternative approaches. Considering the additional requirements for the quality of the representation, the presented algorithm furthermore convinces in the visual results. Consequently, our approach demonstrates very promising performance for real-time rendering of rapidly changing feature-rich datasets. Acknowledgements: This work was supported by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Centre “Invasive Computing (SFB/TR 89). It is partially based on work supported by Award No. UK-c0020, made by the King Abdullah University of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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