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Weber, Christopher ;
Hahmann, Stefanie ;
Hagen, Hans
Methods for Feature Detection in Point Clouds
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Abstract
This paper gives an overview over several techniques for detection of features, and in particular sharp features, on point-sampled geometry. In addition, a new technique using the Gauss map is shown. Given an unstructured point cloud, this method computes a Gauss map clustering on local neighborhoods in order to discard all points that are unlikely to belong to a sharp feature. A single parameter is used in this stage to control the sensitivity of the feature detection. BibTeX - Entry
@InProceedings{weber_et_al:OASIcs:2011:3101,
author = {Christopher Weber and Stefanie Hahmann and Hans Hagen},
title = {{Methods for Feature Detection in Point Clouds}},
booktitle = {Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop)},
pages = {90--99},
series = {OpenAccess Series in Informatics (OASIcs)},
ISBN = {978-3-939897-29-3},
ISSN = {2190-6807},
year = {2011},
volume = {19},
editor = {Ariane Middel and Inga Scheler and Hans Hagen},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2011/3101},
URN = {urn:nbn:de:0030-drops-31018},
doi = {http://dx.doi.org/10.4230/OASIcs.VLUDS.2010.90},
annote = {Keywords: point cloud, sharp features, reconstruction, Gaussmap, clustering}
}
Keywords:
point cloud, sharp features, reconstruction, Gaussmap, clustering
Seminar:
Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop)
Issue Date:
2011
Date of publication:
13.04.2011
