论文标题
数据驱动的空间填充曲线
Data-Driven Space-Filling Curves
论文作者
论文摘要
我们为2D和3D可视化提出了数据驱动的空间填充曲线方法。我们的柔性曲线以与现有方法相比可以更好地保留太空中的空间域中的数据元素。我们通过计算一条哈密顿路径来实现此类数据相干性，该路径大致将描述数据值和位置相干性相似性的目标函数最小化。我们的扩展变体甚至通过Quadtrees和Octrees支持多尺度数据。我们的方法在可视化的许多领域很有用，包括多元或比较可视化，常规网格上的2D和3D数据的集合可视化或粒子模拟的多尺度视觉分析。通过与现有技术的数值比较以及合奏和多元数据集的示例，评估了我们方法的有效性。
We propose a data-driven space-filling curve method for 2D and 3D visualization. Our flexible curve traverses the data elements in the spatial domain in a way that the resulting linearization better preserves features in space compared to existing methods. We achieve such data coherency by calculating a Hamiltonian path that approximately minimizes an objective function that describes the similarity of data values and location coherency in a neighborhood. Our extended variant even supports multiscale data via quadtrees and octrees. Our method is useful in many areas of visualization, including multivariate or comparative visualization, ensemble visualization of 2D and 3D data on regular grids, or multiscale visual analysis of particle simulations. The effectiveness of our method is evaluated with numerical comparisons to existing techniques and through examples of ensemble and multivariate datasets.
