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Methods for Feature Detection in Point Clouds

Authors: Christopher Weber, Stefanie Hahmann, and Hans Hagen

Published in: OASIcs, Volume 19, Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop) (2011)


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.

Cite as

Christopher Weber, Stefanie Hahmann, and Hans Hagen. Methods for Feature Detection in Point Clouds. In Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop). Open Access Series in Informatics (OASIcs), Volume 19, pp. 90-99, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)


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@InProceedings{weber_et_al:OASIcs.VLUDS.2010.90,
  author =	{Weber, Christopher and Hahmann, Stefanie and Hagen, Hans},
  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 =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-29-3},
  ISSN =	{2190-6807},
  year =	{2011},
  volume =	{19},
  editor =	{Middel, Ariane and Scheler, Inga and Hagen, Hans},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.VLUDS.2010.90},
  URN =		{urn:nbn:de:0030-drops-31018},
  doi =		{10.4230/OASIcs.VLUDS.2010.90},
  annote =	{Keywords: point cloud, sharp features, reconstruction, Gaussmap, clustering}
}
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