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Conference papers

Methods for Feature Detection in Point Clouds

Christopher Weber 1 Stefanie Hahmann 2 Hans Hagen 1
1 Department of Computer Science [Kaiserslautern]
2 EVASION - Virtual environments for animation and image synthesis of natural objects
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
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.
Keywords : point cloud sharp features reconstruction Gaussmap clustering
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Conference papers
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https://hal.inria.fr/hal-00921790
Contributor : Brigitte Bidégaray-Fesquet <>
Submitted on : Saturday, December 21, 2013 - 1:35:02 PM
Last modification on : Tuesday, February 9, 2021 - 3:28:03 PM

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LJK_GI_EVASION | LJK_GI | LJK | CNRS | INRIA | UGA

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Christopher Weber, Stefanie Hahmann, Hans Hagen. Methods for Feature Detection in Point Clouds. Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop), Mar 2010, Bodega Bay, CA, United States. pp.90-99, ⟨10.4230/OASIcs.VLUDS.2010.90⟩. ⟨hal-00921790⟩

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