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    Optimal Surface Fitting of Point Clouds Using Local Refinement

    Application to GIS Data

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    Author(s)
    Kermarrec, Gaël
    Skytt, Vibeke
    Dokken, Tor
    Language
    English
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    Abstract
    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.
    URI
    https://library.oapen.org/handle/20.500.12657/60790
    Keywords
    Surface Modeling; Optimum Point Cloud Approximation; Akaike Information Criterion; LR B-Splines; Contour Curves Determination; Deformation Analysis; Bathymetry data
    DOI
    10.1007/978-3-031-16954-0
    ISBN
    9783031169540, 9783031169540
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Cham, 2023
    Imprint
    Springer International Publishing
    Series
    SpringerBriefs in Earth System Sciences,
    Classification
    Information technology: general issues
    Geography
    Numerical analysis
    Mathematical & statistical software
    Pages
    111
    Rights
    http://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    Credits

    • logo Scoss
    • logo EU
    • logo Scoss
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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