CPU体绘制自适应网格细化数据

I. Wald, Carson Brownlee, W. Usher, A. Knoll
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引用次数: 17

摘要

自适应网格细化(AMR)方法在科学计算中广泛应用,使用高效准确的呈现方法将结果数据可视化对于实现交互式数据探索至关重要。在这项工作中,我们详细介绍了在OSPRay交互式CPU光线跟踪框架中直接体绘制块结构(Berger-Colella) AMR数据的综合解决方案。特别是,我们提供了一种使用kd-tree结构表示和遍历AMR数据的通用方法,以及四种不同的重建选项,其中一种(基函数方法)与现有方法相比是新颖的。我们在两种类型的块结构AMR数据和压缩标量字段数据上演示了我们的系统,并展示了如何通过在广泛使用的可视化程序ParaView中的原型集成,轻松地在现有的生产就绪应用程序中使用它。
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CPU volume rendering of adaptive mesh refinement data
Adaptive Mesh Refinement (AMR) methods are widespread in scientific computing, and visualizing the resulting data with efficient and accurate rendering methods can be vital for enabling interactive data exploration. In this work, we detail a comprehensive solution for directly volume rendering block-structured (Berger-Colella) AMR data in the OSPRay interactive CPU ray tracing framework. In particular, we contribute a general method for representing and traversing AMR data using a kd-tree structure, and four different reconstruction options, one of which in particular (the basis function approach) is novel compared to existing methods. We demonstrate our system on two types of block-structured AMR data and compressed scalar field data, and show how it can be easily used in existing production-ready applications through a prototypical integration in the widely used visualization program ParaView.
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