RIA: Data Reduction and New Visualization Techniques for Three-Dimensional Data Sets

RIA：三维数据集的数据缩减和新可视化技术

• 批准号: 9210439
• 负责人: Bernd Hamann
• 金额: $ 11.5万
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-09-01 至 1996-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9210439&HistoricalAwards=false
• 关键词: RIA; Data; Reduction; New; Visualization

This research is concerned with data reduction and general visualization techniques for scalar-valued 3D data sets. Many practical applications deal with 3D data sets where points in space, with associated function values, are given. Such data sets occur in medicine (tomography), geosciences and physical sciences (temperature, pressure, radiation), and several other sciences and disciplines of engineering measurements or mathematical simulations. Generally, such data sets are huge, aggravating the tasks of data storage, interactive data manipulation, and real-time data visualization. Discrete scalar-valued data sets can be reduced by considering derivative information at the given data points. Curvature information of graphs of functions locally approximating the data can be used to define a measure of the significance of single data points can be weighted and removed iteratively form the 3D point set. The proposed methods for 3D points, and 3D retriangulation strategies. The result of reduction algorithm is a data set consisting of scattered 3D points and an associated triangulation (tetrahedra). Common visualization techniques require structured data sets, points typically being organized in a rectilinear ("cuberille") grid. This research will introduce visualization methodologies allowing discretized scalar fields, with arbitrarily located data points, to be rendered.

这项研究涉及数据简化和一般 标量值3D数据集的可视化技术。 许多 实际应用处理3D数据集， 空间，以及相关的函数值。 这样的数据 集发生在医学（断层扫描），地球科学和物理 科学（温度，压力，辐射），以及其他一些 工程测量的科学和学科，或 数学模拟 一般来说，这样的数据集是巨大的， 加重了数据存储、交互数据 操作和实时数据可视化。 离散标量值数据集可以通过考虑 在给定的数据点的衍生信息。 曲率 局部逼近数据的函数图的信息 可用于定义单个重要性的度量 数据点可以被加权并迭代地从3D 点集。 所提出的3D点和3D 再三角化策略 约简算法的结果是 由分散的3D点和相关的 三角测量（四面体）。 常见的可视化技术需要结构化的数据集， 通常以直线（“cuberille”）组织的点 网格 本研究将介绍可视化方法 允许离散标量场，具有任意位置的数据 点，要渲染。