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• W2022204099 startingPage "150" @default.
• W2022204099 abstract "In this paper, we propose a hierarchical approach to 3D scattered data interpolation and approximation with compactly supported radial basis functions. Our numerical experiments suggest that the approach integrates the best aspects of scattered data fitting with locally and globally supported basis functions. Employing locally supported functions leads to an efficient computational procedure, while a coarse-to-fine hierarchy makes our method insensitive to the density of scattered data and allows us to restore large parts of missed data. Given a point cloud distributed over a surface, we first use spatial down sampling to construct a coarse-to-fine hierarchy of point sets. Then we interpolate (approximate) the sets starting from the coarsest level. We interpolate (approximate) a point set of the hierarchy, as an offsetting of the interpolating function computed at the previous level. The resulting fitting procedure is fast, memory efficient, and easy to implement." @default.
• W2022204099 created "2016-06-24" @default.
• W2022204099 creator A5011735957 @default.
• W2022204099 creator A5049557830 @default.
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• W2022204099 date "2005-05-01" @default.
• W2022204099 modified "2023-10-14" @default.
• W2022204099 title "3D scattered data interpolation and approximation with multilevel compactly supported RBFs" @default.
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• W2022204099 doi "https://doi.org/10.1016/j.gmod.2004.06.003" @default.
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