An Analysis of Scalable GPU-Based Ray-Guided Volume Rendering.
An Analysis of Scalable GPU-Based Ray-Guided Volume Rendering.
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DOI：
10.1109/ldav.2013.6675157
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发表时间：
2013-10
期刊：
影响因子：
--
通讯作者：
Krüger J
中科院分区：
文献类型：
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作者：
Fogal T;Schiewe A;Krüger J
在生物医学工程和计算流体力学等不同学科中，体绘制仍然是分析大规模标量场的关键方法。商用桌面硬件一直在努力跟上数据大小增长的步伐，这对现代可视化软件提出了挑战，无法为体绘制等O(N3)算法提供响应的交互。我们的目标是这些领域中常见的数据类型：规则结构的数据。在这项工作中，我们证明了大多数体绘制方法的主要局限性是它们不能快速切换数据采样率(从而数据大小)。使用受最近工作启发的体积渲染器，我们演示了场景的实际可视化数据量通常比商用GPU上的可用内存低得多。我们的仪表化渲染器用于研究体积渲染文献中通常隐藏在地毯下的设计决策。该呈现器是免费提供的，包括适用于所有主要平台的二进制文件以及完整的源代码，以鼓励复制并与未来的研究进行比较。
Volume rendering continues to be a critical method for analyzing large-scale scalar fields, in disciplines as diverse as biomedical engineering and computational fluid dynamics. Commodity desktop hardware has struggled to keep pace with data size increases, challenging modern visualization software to deliver responsive interactions for O(N3) algorithms such as volume rendering. We target the data type common in these domains: regularly-structured data. In this work, we demonstrate that the major limitation of most volume rendering approaches is their inability to switch the data sampling rate (and thus data size) quickly. Using a volume renderer inspired by recent work, we demonstrate that the actual amount of visualizable data for a scene is typically bound considerably lower than the memory available on a commodity GPU. Our instrumented renderer is used to investigate design decisions typically swept under the rug in volume rendering literature. The renderer is freely available, with binaries for all major platforms as well as full source code, to encourage reproduction and comparison with future research.