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Computing Morse-Smale Complexes with Accurate Geometry

机译:用精确的几何计算摩尔斯-斯马德复合体

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摘要

Topological techniques have proven highly successful in analyzing and visualizing scientific data. As a result, significant efforts have been made to compute structures like the Morse-Smale complex as robustly and efficiently as possible. However, the resulting algorithms, while topologically consistent, often produce incorrect connectivity as well as poor geometry. These problems may compromise or even invalidate any subsequent analysis. Moreover, such techniques may fail to improve even when the resolution of the domain mesh is increased, thus producing potentially incorrect results even for highly resolved functions. To address these problems we introduce two new algorithms: (i) a randomized algorithm to compute the discrete gradient of a scalar field that converges under refinement; and (ii) a deterministic variant which directly computes accurate geometry and thus correct connectivity of the MS complex. The first algorithm converges in the sense that on average it produces the correct result and its standard deviation approaches zero with increasing mesh resolution. The second algorithm uses two ordered traversals of the function to integrate the probabilities of the first to extract correct (near optimal) geometry and connectivity. We present an extensive empirical study using both synthetic and real-world data and demonstrates the advantages of our algorithms in comparison with several popular approaches.
机译:事实证明,拓扑技术在分析和可视化科学数据方面非常成功。结果,已经做出了巨大的努力来尽可能健壮和有效地计算诸如摩尔斯-斯马德复合体的结构。但是,所得算法虽然在拓扑上是一致的,但通常会产生错误的连接以及不良的几何形状。这些问题可能会损害甚至使以后的分析无效。而且,即使增加域网格的分辨率,此类技术也可能无法改进,从而即使对于高度解析的函数也可能产生潜在的错误结果。为了解决这些问题,我们引入了两种新算法:(i)一种随机算法,用于计算在精化下收敛的标量场的离散梯度; (ii)确定性变量,可以直接计算准确的几何形状,从而正确计算MS复杂性的连接性。第一种算法在某种意义上收敛,即平均而言它会产生正确的结果,并且其标准偏差会随着网格分辨率的提高而接近零。第二种算法使用函数的两个有序遍历来集成第一种算法的概率,以提取正确(接近最佳)的几何形状和连通性。我们使用综合和真实数据进行了广泛的实证研究,并展示了与几种流行方法相比我们的算法的优势。

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