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Hardware-assisted feature analysis and visualization of procedurally encoded multifield volumetric data

机译:程序编码的多场体数据的硬件辅助特征分析和可视化

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摘要

We take a new approach to interactive visualization and feature detection of large scalar, vector, and multifield computational fluid dynamics data sets that is also well suited for meshless CFD methods. Radial basis functions (RBFs) are used to procedurally encode both scattered and irregular gridded scalar data sets. The RBF encoding creates a complete, unified, functional representation of the scalar field throughout 3D space, independent of the underlying data topology, and eliminates the need for the original data grid during visualization. The capability of commodity PC graphics hardware to accelerate the reconstruction and rendering and to perform feature detection from this functional representation is a powerful tool for visualizing procedurally encoded volumes. Our RBF encoding and GPU-accelerated reconstruction, feature detection, and visualization tool provides a flexible system for visually exploring and analyzing large, structured, scattered, and unstructured scalar, vector, and multifield data sets at interactive rates on desktop PCs.
机译:我们采用了一种新的方法来进行交互式可视化和大型标量,矢量和多场计算流体动力学数据集的特征检测,该方法也非常适合无网格CFD方法。径向基函数(RBF)用于对散乱和不规则网格标量数据集进行程序编码。 RBF编码独立于基础数据拓扑结构,在整个3D空间中创建了标量场的完整,统一,功能性表示,并且在可视化过程中无需使用原始数据网格。商用PC图形硬件加速该功能表示的重建和渲染以及执行功能检测的功能,是使过程编码的体积可视化的强大工具。我们的RBF编码和GPU加速的重建,特征检测和可视化工具为在台式PC上以交互速率可视化地探索和分析大型,结构化,分散和非结构化的标量,矢量和多字段数据集提供了一种灵活的系统。

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