Data-Driven Space-Filling Curves

Liang Zhou; Chris R. Johnson; Daniel Weiskopf

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Data-Driven Space-Filling Curves

机译：Data-Driven Space-Filling Curves

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摘要

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.

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来源
《IEEE transactions on visualization and computer graphics》 |2021年第2期|1591-1600|共10页
作者
Liang Zhou; Chris R. Johnson; Daniel Weiskopf;
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作者单位

National Institute of Health Data Science, Peking University;

SCI Institute, University of Utah;

Visualization Research Center (VISUS), University of Stuttgart;

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原文格式 PDF
正文语种英语
中图分类模式识别与装置;
关键词
Three-dimensional displays; Two dimensional displays; Data visualization; Brushes; Linear programming; Rendering (computer graphics); Faces; Space-filling curves; comparative visualization; ensemble visualization; multivariate visualization;

Data-Driven Space-Filling Curves

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