Feature tracking in time-varying scalar fields is a fundamental task in scientific computing. Topological descriptors, which summarize important features of data, have proved to be viable tools to facilitate this task. The merge tree is a topological descriptor that captures the connectivity behaviors of the sub- or superlevel sets of a scalar field. Edit distances between merge trees play a vital role in effective temporal data tracking. Existing methods to compute them fall into two main classes, namely whether they are dependent or independent of the branch decomposition. These two classes represent the most prominent approaches for producing tracking results. In this paper, we compare four different merge tree edit distance-based methods for feature tracking. We demonstrate that these methods yield distinct results with both analytical and real-world data sets. Furthermore, we investigate how these results vary and identify the factors that influence them. Our experiments reveal significant differences in tracked features over time, even among those produced by techniques within the same category.

翻译：时变标量场中的特征跟踪是科学计算中的一项基础任务。拓扑描述符作为数据重要特征的概括性表示，已被证明是辅助该任务的有效工具。合并树是一种拓扑描述符，能够捕捉标量场子水平集或超水平集的连通性行为。合并树间的编辑距离在有效的时序数据跟踪中起着至关重要的作用。现有计算方法主要分为两大类：依赖于分支分解的方法与独立于分支分解的方法。这两类方法代表了当前生成跟踪结果的主流技术路径。本文比较了四种基于合并树编辑距离的特征跟踪方法。我们通过解析数据集与真实数据集证明，这些方法会产生显著不同的跟踪结果。此外，我们深入探究了这些结果的差异模式，并识别了影响结果的关键因素。实验表明，即使在同一类别的方法中，不同技术所跟踪的特征随时间演变也呈现出显著差异。