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

TL;DR

This paper introduces a single-pass GPU-raycasting algorithm for SAMR data using a kD-tree encoded in 3D-textures, enabling efficient, fully GPU-based adaptive sampling without CPU interaction.

Contribution

It presents a novel single-pass GPU-raycasting method for SAMR data that overcomes previous multi-pass limitations by encoding the kD-tree in 3D-textures.

Findings

Efficient GPU-based sampling of SAMR data.

Reduction of rendering passes from multiple to one.

Improved support for advanced lighting schemes.

Abstract

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 a 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.