论文标题
VOREEN - 用于交互式可视化和处理大量数据的开源框架
Voreen -- An Open-source Framework for Interactive Visualization and Processing of Large Volume Data
论文作者
论文摘要
在生物学,医学,物理和地球科学等许多研究领域,测量或模拟量数据的技术进步导致了大量数据大小。在这里,大数据可以参考具有高空间和/或时间分辨率的单个数据集,以及在同时或合奏意义上的数据集集合。因此,通用和可自定义的体积可视化和处理系统必须提供止回机制,以便处理和分析此类数据。 Voreen是一个开源快速型型框架,最初旨在快速使用相当常见的数据流图范式来快速为体积成像数据创建自定义可视化应用程序。近年来,Voreen已用于各种跨学科研究项目,对大型数据处理能力的需求不断增加,而无需依赖集群计算资源。因此,在其最新版本中,Voreen通过较高的空间分辨率处理和可视化数量数据以及包括时空多场仿真集合(包括时空数据集)的集合数据进行了扩展。在本文中,我们比较了最先进的数量处理和可视化系统,并得出结论,Voreen是第一个结合核心外处理和渲染功能的系统,用于大量消费者硬件和对跨学科研究重要的功能。我们描述了Voreen如何实现这些目标并显示其使用,绩效和能力,以通过在两个大批量数据研究中介绍典型的工作流来支持跨学科研究。
Technological advances for measuring or simulating volume data have led to large data sizes in many research areas such as biology, medicine, physics, and geoscience. Here, large data can refer to individual data sets with high spatial and/or temporal resolution as well as collections of data sets in the sense of cohorts or ensembles. Therefore, general-purpose and customizable volume visualization and processing systems have to provide out-of-core mechanisms that allow for handling and analyzing such data. Voreen is an open-source rapid-prototyping framework that was originally designed to quickly create custom visualization applications for volumetric imaging data using the meanwhile quite common data flow graph paradigm. In recent years, Voreen has been used in various interdisciplinary research projects with an increasing demand for large data processing capabilities without relying on cluster compute resources. In its latest release, Voreen has thus been extended by out-of-core techniques for processing and visualization of volume data with very high spatial resolution as well as collections of volume data sets including spatio-temporal multi-field simulation ensembles. In this paper we compare state-of-the-art volume processing and visualization systems and conclude that Voreen is the first system combining out-of-core processing and rendering capabilities for large volume data on consumer hardware with features important for interdisciplinary research. We describe how Voreen achieves these goals and show-case its use, performance, and capability to support interdisciplinary research by presenting typical workflows within two large volume data case studies.