A REVIEW OF POINT CLOUDS SEGMENTATION AND CLASSIFICATION ALGORITHMS

Today 3D models and point clouds are very popular being currently used in several fields, shared through the internet and even accessed on mobile phones. Despite their broad availability, there is still a relevant need of methods, preferably automatic, to provide 3D data with meaningful attributes t...

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Veröffentlicht in:International archives of the photogrammetry, remote sensing and spatial information sciences. Jg. XLII-2/W3; S. 339 - 344
Hauptverfasser: Grilli, E., Menna, F., Remondino, F.
Format: Journal Article Tagungsbericht
Sprache:Englisch
Veröffentlicht: Gottingen Copernicus GmbH 23.02.2017
Copernicus Publications
Schlagworte:
Algorithms
Classification
Cultural heritage
Cultural resources
Regions
Segmentation
Solutions
Three dimensional models
ISSN:2194-9034, 1682-1750, 2194-9034
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Zusammenfassung:Today 3D models and point clouds are very popular being currently used in several fields, shared through the internet and even accessed on mobile phones. Despite their broad availability, there is still a relevant need of methods, preferably automatic, to provide 3D data with meaningful attributes that characterize and provide significance to the objects represented in 3D. Segmentation is the process of grouping point clouds into multiple homogeneous regions with similar properties whereas classification is the step that labels these regions. The main goal of this paper is to analyse the most popular methodologies and algorithms to segment and classify 3D point clouds. Strong and weak points of the different solutions presented in literature or implemented in commercial software will be listed and shortly explained. For some algorithms, the results of the segmentation and classification is shown using real examples at different scale in the Cultural Heritage field. Finally, open issues and research topics will be discussed.
Bibliographie:ObjectType-Article-1
ObjectType-Feature-2
SourceType-Conference Papers & Proceedings-1
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ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLII-2-W3-339-2017