Surface Extraction from Multi-field Particle Volume Data Using Multi-dimensional Cluster Visualization
Surface Extraction from Multi-field Particle Volume Data Using Multi-dimensional Cluster Visualization
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DOI：
10.1109/tvcg.2008.167
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发表时间：
2008-11
影响因子：
5.2
通讯作者：
L. Linsen;Tran Van Long;Paul Rosenthal;S. Rosswog
中科院分区：
文献类型：
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作者：
L. Linsen;Tran Van Long;Paul Rosenthal;S. Rosswog
由物理模拟产生的数据集通常包含大量物理变量。因此，希望可视化方法考虑整个多场体积数据，而不是集中在一个变量上。提出了一种基于多场粒子体数据表面提取的可视化方法。表面相对于底层多变量函数对数据进行分段。分割属性的决定是基于多维特征空间的分析。特征空间的探索是由一个自动化的多维层次聚类方法，其产生的密度集群的密度水平集的形式显示在一个三维星星坐标布局。在星星坐标布局中，用户可以选择感兴趣的聚类。在特征空间中选择的聚类对应于对象空间中的分割表面。基于聚类隶属度所引起的分割特性，我们从体数据中提取出一个表面。我们的驱动程序是平滑粒子流体动力学（SPH）模拟，其中每个粒子携带多个属性。数据集以非结构化的基于点的体数据的形式给出。我们直接从这些数据中提取我们的表面，而无需事先重新定位或网格生成。曲面提取计算曲面上的各个点，这由高效的邻域计算支持。使用基于点的渲染操作渲染所提取的表面点。我们的方法结合了对象空间操作的科学可视化方法和特征空间操作的信息可视化方法。
Data sets resulting from physical simulations typically contain a multitude of physical variables. It is, therefore, desirable that visualization methods take into account the entire multi-field volume data rather than concentrating on one variable. We present a visualization approach based on surface extraction from multi-field particle volume data. The surfaces segment the data with respect to the underlying multi-variate function. Decisions on segmentation properties are based on the analysis of the multi-dimensional feature space. The feature space exploration is performed by an automated multi-dimensional hierarchical clustering method, whose resulting density clusters are shown in the form of density level sets in a 3D star coordinate layout. In the star coordinate layout, the user can select clusters of interest. A selected cluster in feature space corresponds to a segmenting surface in object space. Based on the segmentation property induced by the cluster membership, we extract a surface from the volume data. Our driving applications are smoothed particle hydrodynamics (SPH) simulations, where each particle carries multiple properties. The data sets are given in the form of unstructured point-based volume data. We directly extract our surfaces from such data without prior resampling or grid generation. The surface extraction computes individual points on the surface, which is supported by an efficient neighborhood computation. The extracted surface points are rendered using point-based rendering operations. Our approach combines methods in scientific visualization for object-space operations with methods in information visualization for feature-space operations.