流场可视化的最优视点选择方法

Optimal Viewpoint Selection for Texture-based Flow Visualization

针对流场可视化当前视点下存在的遮挡与可能面临的非交互式环境的问题,提出一种最优视点选择方法.首先,根据当前视点下的流场行为和结构信息及可视投影的图像质量,利用信息理论概念进行最优视点综合性度量;其次,应用可视成像的相关性对视点间的相似性评估,以决定两视点间的最小重叠;最后,结合K均值聚类方法将相似视点归为一类,每一类中视点熵值最高的视点作为最具代表性的视点.应用各种流场数据集进行可视化最佳视点选择实验,给出了视点选择效果图、与其他方法的最优视点效果对比图以及选择过程的计算时间.实验结果表明,该方法能够自动、有效地进行最优视点选择.

Selecting the most representative view and dynamic viewpoint planning are desirable and challenging due to possible occlusions in the current view,especially for highly non-interactive situations.In this paper,we present a“goodness”measure of viewpoints selection designed for texture-based flow visualization and cluster optimal viewpoints for further exploration.Our optimal viewpoint measure considers not only flow behavior and structural information but also projected image quality.We extract a series of optimal viewpoints based on K-means clustering algorithm due to the slow frame-rates or non-interactive process.The experiment results demonstrate that this method can automatically and effectively achieve the optimal viewpoint.