Shape Modeling International (SMI 2010) (2010)
June 21, 2010 to June 23, 2010
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SMI.2010.32
Christopher Weber , Tech. Univ. Kaiserslautern, Kaiserslautern, Germany
Stefanie Hahmann , Lab. Jean Kuntzmann, Univ. de Grenoble, Grenoble, France
Hans Hagen , Tech. Univ. Kaiserslautern, Kaiserslautern, Germany
This paper presents a new technique for detecting sharp features on point-sampled geometry. Sharp features of different nature and possessing angles varying from obtuse to acute can be identified without any user interaction. The algorithm works directly on the point cloud, no surface reconstruction is needed. Given an unstructured point cloud, our method first computes a Gauss map clustering on local neighborhoods in order to discard all points which are unlikely to belong to a sharp feature. As usual, a global sensitivity parameter is used in this stage. In a second stage, the remaining feature candidates undergo a more precise iterative selection process. Central to our method is the automatic computation of an adaptive sensitivity parameter, increasing significantly the reliability and making the identification more robust in the presence of obtuse and acute angles. The algorithm is fast and does not depend on the sampling resolution, since it is based on a local neighbor graph computation.
Computer vision, Clustering algorithms, Gaussian processes, Shape, Surface reconstruction, Feature extraction, Solid modeling, Three-dimensional displays, Sampling methods
C. Weber, S. Hahmann and H. Hagen, "Sharp feature detection in point clouds," Shape Modeling International (SMI 2010)(SMI), Aix-en-Provence, 2010, pp. 175-186.