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Optimal surface fitting of point clouds using local refinementapplication to GIS Data /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimal surface fitting of point clouds using local refinementby Gaël Kermarrec, Vibeke Skytt, Tor Dokken.
Reminder of title:	application to GIS Data /
Author:	Kermarrec, Gael.
other author:	Skytt, Vibeke.
Published:	Cham :Springer International Publishing :2023.
Description:	xix, 111 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Spline theory.
Online resource:	https://doi.org/10.1007/978-3-031-16954-0
ISBN:	9783031169540$q(electronic bk.)

Optimal surface fitting of point clouds using local refinementapplication to GIS Data /
Kermarrec, Gael.

Optimal surface fitting of point clouds using local refinementapplication to GIS Data /[electronic resource] :by Gaël Kermarrec, Vibeke Skytt, Tor Dokken. - Cham :Springer International Publishing :2023. - xix, 111 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in earth system sciences,2191-5903. - SpringerBriefs in earth system sciences..

Introduction -- Locally Refined Splines -- Adaptive surface Fitting with Local Refinement: LR B-spline Surfaces -- A Statistical Criterion to Judge the Goodness of Fit of LR B-splines Surface Approximation -- LR B-splines for Representation of Terrain and Seabed: Data Fusion, Outliers, and Voids -- LR B-spline Surfaces and Volumes for Deformation Analysis of Terrain Data -- Conclusion.

Open access.

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.

ISBN: 9783031169540$q(electronic bk.)

Standard No.: 10.1007/978-3-031-16954-0doiSubjects--Topical Terms:

229127
Spline theory.

LC Class. No.: QA224 / .K47 2023

Dewey Class. No.: 511.4223

Optimal surface fitting of point clouds using local refinementapplication to GIS Data /
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245 1 0 $a Optimal surface fitting of point clouds using local refinement $h [electronic resource] : $b application to GIS Data / $c by Gaël Kermarrec, Vibeke Skytt, Tor Dokken.
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505 0 $a Introduction -- Locally Refined Splines -- Adaptive surface Fitting with Local Refinement: LR B-spline Surfaces -- A Statistical Criterion to Judge the Goodness of Fit of LR B-splines Surface Approximation -- LR B-splines for Representation of Terrain and Seabed: Data Fusion, Outliers, and Voids -- LR B-spline Surfaces and Volumes for Deformation Analysis of Terrain Data -- Conclusion.
506 $a Open access.
520 $a 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.
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