标题： 评估基于曲线的矢量场处理中的邻域搜索 显示英文标题

标题： Evaluate Neighbor Search for Curve-based Vector Field Processing

摘要： 基于曲线的表示方法，特别是积分曲线，常用于表示大规模计算流体动力学模拟。 处理和分析基于曲线的矢量场数据集通常涉及在给定查询点或曲线段的情况下搜索相邻的线段。 然而，由于原始流动行为可能无法由积分曲线集合完全表示，且输入的积分曲线在空间中可能分布不均，因此流行的邻近搜索策略常常返回偏斜且冗余的相邻线段。 然而，关于特定邻近搜索策略返回的不同邻近线段配置如何影响后续任务，目前缺乏系统且全面的研究。 为了填补这一空白，本研究在基于点的矢量场重建任务以及使用输入积分曲线的线段显著性估计中，评估了两种流行的邻近搜索策略与不同距离度量的性能。 本研究进行了大量重建测试和显著性计算。 为了对不同搜索策略进行有效的比较，提出了若干度量方法，如平均邻近距离和均匀性。 我们的研究得出了一些观察结果，这些结果部分证实了我们对理想邻域配置的预期，同时揭示了一些社区之前忽略的额外发现。 显示英文摘要

摘要： Curve-based representations, particularly integral curves, are often used to represent large-scale computational fluid dynamic simulations. Processing and analyzing curve-based vector field data sets often involves searching for neighboring segments given a query point or curve segment. However, because the original flow behavior may not be fully represented by the set of integral curves and the input integral curves may not be evenly distributed in space, popular neighbor search strategies often return skewed and redundant neighboring segments. Yet, there is a lack of systematic and comprehensive research on how different configurations of neighboring segments returned by specific neighbor search strategies affect subsequent tasks. To fill this gap, this study evaluates the performance of two popular neighbor search strategies combined with different distance metrics on a point-based vector field reconstruction task and a segment saliency estimation using input integral curves. A large number of reconstruction tests and saliency calculations are conducted for the study. To characterize the configurations of neighboring segments for an effective comparison of different search strategies, a number of measures, like average neighbor distance and uniformity, are proposed. Our study leads to a few observations that partially confirm our expectations about the ideal configurations of a neighborhood while revealing additional findings that were overlooked by the community.