A Survey of GPU-Based Large-Scale Volume Visualization
A Survey of GPU-Based Large-Scale Volume Visualization
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DOI：
10.2312/eurovisstar.20141175
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Johanna Beyer;Markus Hadwiger;H. Pfister
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文献类型：
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作者：
Johanna Beyer;Markus Hadwiger;H. Pfister
本调查给出了在交互式大规模体可视化的GPU技术的当前状态的概述。该领域的现代技术已经在如何在GPU上启用千兆字节、兆字节和千兆字节体积数据的交互式可视化和分析方面带来了巨大的变化。除了将GPU的并行处理能力与核外方法和数据流相结合之外，交互性的一个主要推动因素是使计算和可视化工作与屏幕上实际可见的数据的数量和分辨率成比例，即，“输出敏感”算法和系统设计。这导致了最近的输出敏感的方法是“射线引导”，“可视化驱动”或“显示感知”。在这次调查中，我们专注于这些特点，并提出了一个新的分类基于GPU的大规模体可视化技术的概念的基础上，实际的输出分辨率的可见性和当前的工作集的体积砖的当前子集的数据，最低限度地需要产生所需的显示分辨率的输出图像。出于我们在这里的目的，我们将使用集群的并行（分布式）可视化视为一组正交技术，我们不详细讨论，但可以与我们在本调查中讨论的内容结合使用。
This survey gives an overview of the current state of the art in GPU techniques for interactive large-scale volume visualization. Modern techniques in this field have brought about a sea change in how interactive visualization and analysis of giga-, tera-, and petabytes of volume data can be enabled on GPUs. In addition to combining the parallel processing power of GPUs with out-of-core methods and data streaming, a major enabler for interactivity is making both the computational and the visualization effort proportional to the amount and resolution of data that is actually visible on screen, i.e., “output-sensitive” algorithms and system designs. This leads to recent output-sensitive approaches that are “ray-guided,” “visualization-driven,” or “display-aware.” In this survey, we focus on these characteristics and propose a new categorization of GPU-based large-scale volume visualization techniques based on the notions of actual output-resolution visibility and the current working set of volume bricks—the current subset of data that is minimally required to produce an output image of the desired display resolution. For our purposes here, we view parallel (distributed) visualization using clusters as an orthogonal set of techniques that we do not discuss in detail but that can be used in conjunction with what we discuss in this survey.