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).

计算技术的进步使模拟对当今的科学至关重要，就像过去的理论和实验一样。不幸的是，这一成功是通过淹没科学家的数据来压倒科学界的巨大。由于存储，网络和图形瓶颈，生成的数据比可以分析或可视化的数据更多。由于这些缺陷，丢失或忽略了有价值的信息。此外，科学家们痛苦地意识到了这种技术瓶颈，它影响了他们的科学方式。它减慢了数据获取率，并且可以生成更高维数据的计算，例如矢量和张量未进行。该项目的目的是通过开发多分辨率软件工具来解决可视化瓶颈，从而有助于了解大型实验和仿真数据集。然后，该项目将将这些新技术应用于物理和生物学的数据，从而提供流体动力学和神经科学的进步。我们将通过与学科科学家进行密切互动，包括使用他们的反馈来改善我们工具的初始设计。同样很明显，在可预见的将来，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，我们必须开发将这些巨大数据集表示，分割和压缩这些巨大数据集中的方法，以可以在当今的图形计算机上处​​理。多分辨率表示和算法是要开发的方法和工具的核心。一种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中的资源来扩展到巨大的数据集。例如，多分辨率建模和显示技术将使用户可以在粗糙的级别上查看一个数据集，并且仅在最引起最大兴趣的地区轻松提供相关细节。在分析过程中，多分辨率方法可以在数据段中分配更多的计算资源，其变化率最高或包含特定属性。该项目将生产一组多分辨率软件工具，用于处理和可视化大规模的N维（标量，矢量和张量）体积数据集（NDVDS）以及大型三角网格。具体而言，将开发用于使用级别集合方法进行分割和插值NDVD的工具，直接从音量数据集中提取半规则网格，将开发压缩三角网格以及音量渲染NDVD。随着工具的可用，它们将被部署给加州理工学院中心的科学家，以模拟材料的动态响应（DOE赞助），以及国家显微镜和成像研究中心（UC San Diego）的科学家。