结构化自适应网格加密数据的单次GPU光线投射
Single-Pass GPU-Raycasting for Structured Adaptive Mesh Refinement Data
摘要：
结构化自适应网格加密（SAMR）是一种研究高时空动态范围过程的流行数值方法。它通过调整基础微分方程求解的格子来最有效地表示解，从而降低了计算要求。特别是在天体物理学和宇宙学中，这样的模拟现在可以捕捉到相隔十个数量级甚至更多的空间尺度。SAMR方案中细化区域的不规则位置和扩展以及不同分辨率水平部分重叠的事实，对基于GPU的直接体绘制方法提出了挑战。kD树已被证明有利于将数据域细分为大小相等的单元的非重叠块，这对于当前图形硬件的纹理单元是最佳的，但是使用该数据结构的用于SAMR数据的先前GPU支持的光线投射方法需要针对每个节点的单独渲染通道，从而阻止了需要同时访问多于一个单元块的许多高级照明方案的应用。在本文中，我们提出了一个单通道GPU光线投射算法的SAMR数据，是基于kD树。该树由一组3D纹理有效编码，这允许完全在GPU上自适应地采样完整的光线，而无需任何CPU交互。我们讨论了两种不同的数据存储策略来访问GPU上的网格数据，并将其应用于几个数据集，以证明所提出的方法的好处。
英文摘要：
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.
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