Fakultät Informatik

Visualization of Big SPH Simulations via Compressed Octree Grids

 Florian Reichl,  Marc Treib, Rüdiger Westermann

Technische Universität München, Germany.

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Background

Interactive and high-quality visualization of spatially continuous 3D fields represented by scattered distributions of billions of particles is challenging.  One common approach is to resample the quantities carried by the particles to a regular grid and to render the grid via volume ray-casting. In large-scale applications such as astrophysics, however, the required grid resolution can easily exceed 10K samples per spatial dimension, letting resampling approaches appear unfeasible. In this paper we demonstrate that even in these extreme cases such approaches perform surprisingly well, both in terms of memory requirement and rendering performance. We resample the particle data to a multiresolution multiblock grid, where the resolution of the blocks is dictated by the particle distribution.  From this structure we build an octree grid, and we then compress each block in the hierarchy at no visual loss using wavelet-based compression. Since decompression can be performed on the GPU, it can be integrated effectively into GPU-based out-of-core volume ray-casting. We compare our approach to the perspective grid approach which resamples at run-time into a view-aligned grid. We demonstrate considerably faster rendering times at high quality, at only a moderate memory increase compared to the raw particle set.

Acknowledgments

We would like to thank Volker Springel from the Max Planck Society in Garching for his support with the data set. This publication is based on work supported by Award No. UK-C0020, made by King Abdullah University of Science and Technology
(KAUST).

Associated publications

Visualization of Big SPH Simulations via Compressed Octree Grids
F. Reichl, M. Treib, R. Westermann
Proceedings of  IEEE Big Data 2013, Pages 71 - 78 [PDF] [Video] [Bibtex]

 

Submission Video

Results

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A view into the Millennium Run
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Comparison of the uncompressed (left) data to verify the quality of lossy compression (right)