Methods for Feature Detection in Point Clouds

Christopher Weber 1 Stefanie Hahmann 2 Hans Hagen 1
2 EVASION - Virtual environments for animation and image synthesis of natural objects
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
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.
Type de document :
Communication dans un congrès
Ariane Middel and Inga Scheler and Hans Hagen. Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop), Mar 2010, Bodega Bay, CA, United States. Schloss Dagstuhl-Leiniz-Zentrum fuer Informatik, 19, pp.90-99, 2011, OpenAccess Series in Informatics (OASIcs). 〈10.4230/OASIcs.VLUDS.2010.90〉
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https://hal.inria.fr/hal-00921790
Contributeur : Brigitte Bidégaray-Fesquet <>
Soumis le : samedi 21 décembre 2013 - 13:35:02
Dernière modification le : jeudi 11 janvier 2018 - 01:48:42

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Christopher Weber, Stefanie Hahmann, Hans Hagen. Methods for Feature Detection in Point Clouds. Ariane Middel and Inga Scheler and Hans Hagen. Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop), Mar 2010, Bodega Bay, CA, United States. Schloss Dagstuhl-Leiniz-Zentrum fuer Informatik, 19, pp.90-99, 2011, OpenAccess Series in Informatics (OASIcs). 〈10.4230/OASIcs.VLUDS.2010.90〉. 〈hal-00921790〉

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