License: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license (CC BY-NC-ND 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.VLUDS.2010.90
URN: urn:nbn:de:0030-drops-31018
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2011/3101/
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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 =		{10.4230/OASIcs.VLUDS.2010.90},
  annote =	{Keywords: point cloud, sharp features, reconstruction, Gaussmap, clustering}
}

Keywords: point cloud, sharp features, reconstruction, Gaussmap, clustering
Collection: 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

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