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.
@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.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} }
Feedback for Dagstuhl Publishing