978-3-031-16954-0.pdf

This open access book provides insights into the novel Locally Refined B-spline (LR B-spline) surface format, which is suited for representing terrain and seabed data in a compact way. It provides an alternative to the well know raster and triangulated surface representations. An LR B-spline surface...

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Γλώσσα:English
Έκδοση: Springer Nature 2023
Διαθέσιμο Online:https://link.springer.com/978-3-031-16954-0
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spelling oapen-20.500.12657-607902024-03-27T14:15:06Z Optimal Surface Fitting of Point Clouds Using Local Refinement Kermarrec, Gaël Skytt, Vibeke Dokken, Tor Surface Modeling Optimum Point Cloud Approximation Akaike Information Criterion LR B-Splines Contour Curves Determination Deformation Analysis Bathymetry data thema EDItEUR::U Computing and Information Technology::UB Information technology: general topics thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography thema EDItEUR::P Mathematics and Science::PB Mathematics::PBK Calculus and mathematical analysis::PBKS Numerical analysis thema EDItEUR::U Computing and Information Technology::UF Business applications::UFM Mathematical and statistical software This open access book provides insights into the novel Locally Refined B-spline (LR B-spline) surface format, which is suited for representing terrain and seabed data in a compact way. It provides an alternative to the well know raster and triangulated surface representations. An LR B-spline surface has an overall smooth behavior and allows the modeling of local details with only a limited growth in data volume. In regions where many data points belong to the same smooth area, LR B-splines allow a very lean representation of the shape by locally adapting the resolution of the spline space to the size and local shape variations of the region. The iterative method can be modified to improve the accuracy in particular domains of a point cloud. The use of statistical information criterion can help determining the optimal threshold, the number of iterations to perform as well as some parameters of the underlying mathematical functions (degree of the splines, parameter representation). The resulting surfaces are well suited for analysis and computing secondary information such as contour curves and minimum and maximum points. Also deformation analysis are potential applications of fitting point clouds with LR B-splines. 2023-01-20T16:53:32Z 2023-01-20T16:53:32Z 2023 book ONIX_20230120_9783031169540_14 9783031169540 https://library.oapen.org/handle/20.500.12657/60790 eng SpringerBriefs in Earth System Sciences application/pdf n/a 978-3-031-16954-0.pdf https://link.springer.com/978-3-031-16954-0 Springer Nature Springer International Publishing 10.1007/978-3-031-16954-0 10.1007/978-3-031-16954-0 6c6992af-b843-4f46-859c-f6e9998e40d5 e53ce2e9-6435-444c-9239-a3c6446d50d6 872eab38-05f9-4293-b5be-c226b96780ba Deutsche Forschungsgemeinschaft (DFG) 9783031169540 DFG Open Access Publication Funding Springer International Publishing 111 Cham [...] [...] open access
institution OAPEN
collection DSpace
language English
description This open access book provides insights into the novel Locally Refined B-spline (LR B-spline) surface format, which is suited for representing terrain and seabed data in a compact way. It provides an alternative to the well know raster and triangulated surface representations. An LR B-spline surface has an overall smooth behavior and allows the modeling of local details with only a limited growth in data volume. In regions where many data points belong to the same smooth area, LR B-splines allow a very lean representation of the shape by locally adapting the resolution of the spline space to the size and local shape variations of the region. The iterative method can be modified to improve the accuracy in particular domains of a point cloud. The use of statistical information criterion can help determining the optimal threshold, the number of iterations to perform as well as some parameters of the underlying mathematical functions (degree of the splines, parameter representation). The resulting surfaces are well suited for analysis and computing secondary information such as contour curves and minimum and maximum points. Also deformation analysis are potential applications of fitting point clouds with LR B-splines.
title 978-3-031-16954-0.pdf
spellingShingle 978-3-031-16954-0.pdf
title_short 978-3-031-16954-0.pdf
title_full 978-3-031-16954-0.pdf
title_fullStr 978-3-031-16954-0.pdf
title_full_unstemmed 978-3-031-16954-0.pdf
title_sort 978-3-031-16954-0.pdf
publisher Springer Nature
publishDate 2023
url https://link.springer.com/978-3-031-16954-0
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