Zhou Liang, Johnson Chris R, Weiskopf Daniel
IEEE Trans Vis Comput Graph. 2021 Feb;27(2):1591-1600. doi: 10.1109/TVCG.2020.3030473. Epub 2021 Jan 28.
Abstract-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.
摘要——我们提出了一种用于二维和三维可视化的数据驱动空间填充曲线方法。我们的灵活曲线在空间域中遍历数据元素，与现有方法相比，由此产生的线性化能更好地保留空间特征。我们通过计算哈密顿路径来实现这种数据一致性，该路径近似地最小化一个目标函数，该目标函数描述了数据值的相似性和邻域中的位置一致性。我们的扩展变体甚至通过四叉树和八叉树支持多尺度数据。我们的方法在可视化的许多领域都很有用，包括多元或比较可视化、规则网格上二维和三维数据的集成可视化或粒子模拟的多尺度视觉分析。我们通过与现有技术的数值比较以及集成和多元数据集的示例来评估我们方法的有效性。
