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4 February 2013Single-pass GPU-raycasting for structured adaptive mesh refinement data
Structured Adaptive Mesh Refinement (SAMR) is a popular numerical technique to study processes with high spatial and
temporal dynamic range. It reduces computational requirements by adapting the lattice on which the underlying differential
equations are solved to most efficiently represent the solution. Particularly in astrophysics and cosmology such simulations
now can capture spatial scales ten orders of magnitude apart and more. The irregular locations and extensions of the
refined regions in the SAMR scheme and the fact that different resolution levels partially overlap, poses a challenge for
GPU-based direct volume rendering methods. kD-trees have proven to be advantageous to subdivide the data domain into
non-overlapping blocks of equally sized cells, optimal for the texture units of current graphics hardware, but previous
GPU-supported raycasting approaches for SAMR data using this data structure required a separate rendering pass for each
node, preventing the application of many advanced lighting schemes that require simultaneous access to more than one
block of cells. In this paper we present the first single-pass GPU-raycasting algorithm for SAMR data that is based on a
kD-tree. The tree is efficiently encoded by a set of 3D-textures, which allows to adaptively sample complete rays entirely
on the GPU without any CPU interaction. We discuss two different data storage strategies to access the grid data on the
GPU and apply them to several datasets to prove the benefits of the proposed method.
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Ralf Kaehler, Tom Abel, "Single-pass GPU-raycasting for structured adaptive mesh refinement data," Proc. SPIE 8654, Visualization and Data Analysis 2013, 865408 (4 February 2013); https://doi.org/10.1117/12.2008552