Reference : Semi-automatic and versatile point-cloud segmentation by region growing based on norm...
Scientific congresses and symposiums : Poster
Engineering, computing & technology : Architecture
Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography
http://hdl.handle.net/2268/190337
Semi-automatic and versatile point-cloud segmentation by region growing based on normal estimation
English
[fr] Segmentation de nuage de points semi-automatique et versatile par croissance de région basée sur l'estimation de normales
Poux, Florent mailto [Université de Liège > Département de géographie > Unité de Géomatique - Topographie et géométrologie >]
Billen, Roland mailto [Université de Liège > Département de géographie > Unité de Géomatique - Topographie et géométrologie >]
6-Sep-2013
A0
Yes
No
14ème CIPA International Symposium
du 2 septembre au 6 septembre 2013
ICOMOS
ISPRS
Strasbourg
France
[en] point cloud ; segmentation ; laser scanning ; 3D reconstruction ; Feature Extraction ; Cultural Heritage ; Pattern recognition ; Normals
[en] Segmentation is an essential step in the production chain of point-cloud processing. It defines the pertinence and accuracy of a future 3D model reconstruction, as well as a high level analysis of any scene. Its goal is to extract from a large 3D dataset different groups of points that share a logical link to wisely consider them as one entity. Manual segmentation of a 3D point cloud is extremely time consuming and imprecise for large datasets, and thus automated classification is the main focus of this paper.
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/190337

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