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Efficient Simple Large Scattered 3D Vector Fields Radial Basis Functions Approximation Using Space Subdivision

机译:使用空间细分的高效简单大分散3D向量场径向基函数逼近

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The Radial basis function (RBF) approximation is an efficient method for scattered scalar and vector data fields. However its application is very difficult in the case of large scattered data. This paper presents RBF approximation together with space subdivision technique for large vector fields. For large scattered data sets a space subdivision technique with overlapping 3D cells is used. Blending of overlapped 3D cells is used to obtain continuity and smoothness. The proposed method is applicable for scalar and vector data sets as well. Experiments proved applicability of this approach and results with the tornado large vector field data set are presented.
机译:径向基函数(RBF)逼近是分散标量和矢量数据字段的有效方法。但是,在大量分散数据的情况下,其应用非常困难。本文提出了大矢量场的RBF近似和空间细分技术。对于大的分散数据集,使用具有重叠3D单元的空间细分技术。重叠的3D单元的混合用于获得连续性和平滑度。该方法同样适用于标量和矢量数据集。实验证明了该方法的适用性,并给出了龙卷风大矢量场数据集的结果。

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