OASIcs.VLUDS.2010.90.pdf
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Methods for Feature Detection in Point Clouds
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Volume:
Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop) (VLUDS 2010)
Series: Open Access Series in Informatics (OASIcs) - License:
Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license
- Publication Date: 2011-04-13
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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)
https://doi.org/10.4230/OASIcs.VLUDS.2010.90
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.
Subject Classification
Keywords
- point cloud
- sharp features
- reconstruction
- Gaussmap
- clustering
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