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Wyszukujesz frazę "3D point cloud" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Multibeam data processing for 3D object shape reconstruction
Autorzy:
Kulawiak, M.
Łubniewski, Z.
Powiązania:
https://bibliotekanauki.pl/articles/332626.pdf
Data publikacji:
2017
Wydawca:
Polskie Towarzystwo Akustyczne
Tematy:
multibeam sonar
3D reconstruction
point cloud
wrecks
rasterization
Opis:
The technology of hydroacoustic scanning offers an efficient and widely-used source of geospatial information regarding underwater environments, providing measurement data which usually have the structure of irregular groups of points known as point clouds. Since this data model has known disadvantages, a different form of representation based on representing surfaces with simple geometric structures, such as edges and facets, is preferred for data featuring seabed surface relief and various underwater objects. In this paper, the authors propose a multiple-step approach to three-dimensional surface reconstruction from multibeam sonar measurements, relying on the proper application of various algorithms for noise reduction, data rasterization and classification. The results obtained by combining several different surface reconstruction algorithms with the proposed data processing technique were tested, and the strengths and weaknesses of each method were highlighted.
Źródło:
Hydroacoustics; 2017, 20; 105-112
1642-1817
Pojawia się w:
Hydroacoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Methods for quality improvement of multibeam and LiDAR point cloud data in the context of 3D surface reconstruction
Autorzy:
Kulawiak, M.
Łubniewski, Z.
Powiązania:
https://bibliotekanauki.pl/articles/332451.pdf
Data publikacji:
2016
Wydawca:
Polskie Towarzystwo Akustyczne
Tematy:
multibeam sonar
LiDAR
3D
point cloud
shape reconstruction
noise reduction
Opis:
Point cloud dataset is the transitional data model used in several marine and land remote-sensing applications. During further steps of processing, the transformation of point cloud spatial data to more complex models containing higher order geometric structures like edges and facets may be possible, if an appropriate quality level of input data is provided. Point cloud datasets usually contain a considerable amount of undesirable irregularities, such as strong variability of local point density, missing data, overlapping points and noise caused by scattering characteristics of the environment. For these reasons, processing such data can be quite problematic, especially in the field of object detection and threedimensional surface reconstruction. This paper is focused on applying the proposed methods for reducing the mentioned irregularities from several datasets containing 3D point clouds acquired by multibeam sonars and LiDAR scanners. The article also presents the results obtained by each method, and discusses their advantages.
Źródło:
Hydroacoustics; 2016, 19; 251-258
1642-1817
Pojawia się w:
Hydroacoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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