Multiresolution Visualization Tools for Interactive Analysis of Large-Scale N-Dimensional Datasets

用于大规模 N 维数据集交互式分析的多分辨率可视化工具

• 批准号: 9982273
• 负责人: David Breen
• 金额: $ 120万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-10-15 至 2003-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9982273&HistoricalAwards=false
• 关键词: Multiresolution; Visualization; Tools; Interactive; Analysis

Advances in computing technology are making simulation as important to science today as theory and experiment have been in the past. Unfortunately this success is overwhelming the scientific community by drowning scientists in the data produced. Because of storage, network, and graphics bottlenecks, more data is produced than can be analyzed or visualized. Valuable information is being lost or neglected because of these deficiencies. Additionally, scientists are painfully aware of this technological bottleneck, and it affects how they conduct their science. It slows the rate of data acquisition, and calculations that could generate higher-dimensional data, e.g. vector and tensor, are not being performed. The goal of this project is to address the visualization bottleneck by developing multiresolution software tools that will facilitate understanding of large-scale experimental and simulation datasets. The project will then apply these new techniques to data from physics and biology, thus providing advances in fluid dynamics and neuroscience. We will do this by close interactions with the discipline scientists, including using their feedback to improve initial designs of our tools.The technical approach of the project is based on the fact that the graphics workstations available to most scientists are incapable of visualizing giga- and tera-scale datasets. It is also clear that for the foreseeable future our ability to generate data will outpace our ability to render and visualize it. Therefore we must develop methods for representing, segmenting and compressing these enormous datasets into a form that can be processed on today's graphics computers. Multiresolution representations and algorithms are at the core of the methods and tools to be developed. A multiresolution approach allows visualization techniques to scale to enormous datasets by focusing resources in those regions of the dataset most important to the user. For example, multiresolution modeling and display techniques will allow a user to view a single dataset at a coarse level-of--detail, and easily provide pertinent details only in those regions of greatest interest. During analysis, multiresolution methods can allocate more computational resources in segments of the data with the greatest rates of change or containing specific properties. This project will produce a set of multiresolution software tools for processing and visualizing large-scale N-dimensional (scalar, vector and tensor) volumetric datasets (NDVDs), as well as large-scale triangle meshes. Specifically, tools for segmenting and interpolating NDVDs using level-set methods, extracting semi-regular meshes directly from volume datasets, compressing triangle meshes, and volume rendering NDVDs will be developed. As tools become available they will be deployed to scientists at Caltech's Center for Simulation of Dynamic Response of Materials (sponsored by DOE), and to scientists at the National Center for Microscopy and Imaging Research (UC San Diego).

计算技术的进步使得模拟对今天的科学来说就像过去的理论和实验一样重要。不幸的是，这种成功使科学界淹没在所产生的数据中。由于存储、网络和图形瓶颈，产生的数据比可以分析或可视化的数据要多。由于这些缺陷，有价值的信息正在丢失或被忽视。此外，科学家们痛苦地意识到这一技术瓶颈，它影响了他们如何进行科学研究。它降低了数据采集的速度，并且不会执行可能生成更高维数据（例如向量和张量）的计算。该项目的目标是通过开发多分辨率软件工具来解决可视化瓶颈，这将有助于理解大规模的实验和模拟数据集。然后，该项目将把这些新技术应用于物理学和生物学数据，从而在流体动力学和神经科学方面取得进展。我们将通过与学科科学家的密切互动来实现这一目标，包括利用他们的反馈来改进我们工具的初始设计。该项目的技术方法是基于这样一个事实，即大多数科学家可用的图形工作站无法可视化千兆和兆级数据集。同样清楚的是，在可预见的未来，我们生成数据的能力将超过我们渲染和可视化数据的能力，因此，我们必须开发方法来表示、分割和压缩这些庞大的数据集，使其成为可以在今天的图形计算机上处理的形式。多分辨率表示和算法是待开发的方法和工具的核心。多分辨率方法允许可视化技术通过将资源集中在对用户最重要的数据集区域来扩展到巨大的数据集。例如，多分辨率建模和显示技术将允许用户以粗略的细节水平查看单个数据集，并且仅在最感兴趣的那些区域中容易地提供相关细节。在分析过程中，多分辨率方法可以在具有最大变化率或包含特定属性的数据段中分配更多的计算资源。该项目将产生一套多分辨率软件工具，用于处理和可视化大规模N维（标量，矢量和张量）体积数据集（NDVD）以及大规模三角形网格。具体而言，将开发使用水平集方法分割和插值NDVD，直接从体积数据集提取半规则网格，压缩三角形网格和体积渲染NDVD的工具。随着工具的可用，它们将被部署给加州理工学院材料动态响应模拟中心（由DOE赞助）的科学家，以及国家显微镜和成像研究中心（加州大学圣地亚哥分校）的科学家。