Kopp Wiebke, Weinkauf Tino
IEEE Trans Vis Comput Graph. 2023 Jan;29(1):1157-1167. doi: 10.1109/TVCG.2022.3209387. Epub 2022 Dec 16.
Creating a static visualization for a time-dependent scalar field is a non-trivial task, yet very insightful as it shows the dynamics in one picture. Existing approaches are based on a linearization of the domain or on feature tracking. Domain linearizations use space-filling curves to place all sample points into a 1D domain, thereby breaking up individual features. Feature tracking methods explicitly respect feature continuity in space and time, but generally neglect the data context in which those features live. We present a feature-based linearization of the spatial domain that keeps features together and preserves their context by involving all data samples. We use augmented merge trees to linearize the domain and show that our linearized function has the same merge tree as the original data. A greedy optimization scheme aligns the trees over time providing temporal continuity. This leads to a static 2D visualization with one temporal dimension, and all spatial dimensions compressed into one. We compare our method against other domain linearizations as well as feature-tracking approaches, and apply it to several real-world data sets.
为随时间变化的标量场创建静态可视化是一项复杂的任务，但却非常有洞察力，因为它能在一张图中展示动态变化。现有的方法基于域的线性化或特征跟踪。域线性化使用空间填充曲线将所有采样点放置到一维域中，从而破坏了单个特征。特征跟踪方法明确尊重特征在空间和时间上的连续性，但通常忽略了这些特征所在的数据上下文。我们提出了一种基于特征的空间域线性化方法，该方法通过纳入所有数据样本，将特征聚集在一起并保留其上下文。我们使用增强合并树来线性化域，并表明我们的线性化函数与原始数据具有相同的合并树。一种贪婪优化方案使这些树随时间对齐，从而提供时间连续性。这产生了一个具有一个时间维度的静态二维可视化，所有空间维度都压缩到一个维度中。我们将我们的方法与其他域线性化方法以及特征跟踪方法进行比较，并将其应用于几个真实世界的数据集。
