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Structured Adaptive Mesh Refinement (SAMR) is a popular numerical techniqueto study processes with high spatial and temporal dynamic range. It reducescomputational requirements by adapting the lattice on which the underlyingdifferential equations are solved to most efficiently represent the solution.Particularly in astrophysics and cosmology such simulations now can capturespatial scales ten orders of magnitude apart and more. The irregular locationsand extensions of the refined regions in the SAMR scheme and the fact thatdifferent resolution levels partially overlap, poses a challenge for GPU-baseddirect volume rendering methods. kD-trees have proven to be advantageous tosubdivide the data domain into non-overlapping blocks of equally sized cells,optimal for the texture units of current graphics hardware, but previousGPU-supported raycasting approaches for SAMR data using this data structurerequired a separate rendering pass for each node, preventing the application ofmany advanced lighting schemes that require simultaneous access to more thanone block of cells. In this paper we present a single-pass GPU-raycastingalgorithm for SAMR data that is based on a kD-tree. The tree is efficientlyencoded by a set of 3D-textures, which allows to adaptively sample completerays entirely on the GPU without any CPU interaction. We discuss two differentdata storage strategies to access the grid data on the GPU and apply them toseveral datasets to prove the benefits of the proposed method.

Single-Pass GPU-Raycasting for Structured Adaptive Mesh Refinement Data