View-Dependent Streamlines for 3D Vector Fields
View-Dependent Streamlines for 3D Vector Fields
复制标题
3D 矢量场的视图相关流线
DOI：
10.1109/tvcg.2010.212
复制
发表时间：
2010
影响因子：
5.2
通讯作者：
K. Ma
中科院分区：
文献类型：
--
作者：
Stéphane Marchesin;Cheng;Chris Ho;K. Ma
提出了一种新的三维矢量场流线布局和选择算法。我们没有将问题看作是数据空间中的简单特征搜索，而是基于这样的观察，即大多数流线场都会产生大量的自遮挡，这阻碍了正确的可视化。为了避免这个问题，我们以一种依赖于视图的方式来处理这个问题，并动态地确定一组有助于数据理解的流水线，而不会扰乱视图。由于我们的技术结合了流特征标准和依赖于视图的流线选择，我们能够实现这两个领域的最佳效果：相关的流描述和清晰、整洁的图片。我们详细介绍了我们的算法的一个高效的GPU实现，在多个数据集上显示了全面的可视化结果，并与现有的流描述技术进行了比较。我们的结果表明，我们的技术大大提高了不同数据集上的Streamline可视化的可读性，而不需要用户干预。
This paper introduces a new streamline placement and selection algorithm for 3D vector fields. Instead of considering the problem as a simple feature search in data space, we base our work on the observation that most streamline fields generate a lot of self-occlusion which prevents proper visualization. In order to avoid this issue, we approach the problem in a view-dependent fashion and dynamically determine a set of streamlines which contributes to data understanding without cluttering the view. Since our technique couples flow characteristic criteria and view-dependent streamline selection we are able achieve the best of both worlds: relevant flow description and intelligible, uncluttered pictures. We detail an efficient GPU implementation of our algorithm, show comprehensive visual results on multiple datasets and compare our method with existing flow depiction techniques. Our results show that our technique greatly improves the readability of streamline visualizations on different datasets without requiring user intervention.