Abstract
Crust and skeleton approximation from samples
Highlights
Samples of shape boundary carry the information of the shape for a sufficiently dense sampling
There is not any connectivity information between sample points and the geometry or topology of the original shape is not known. These sample points play an important role in many applications for crust and skeleton approximation
We focus on the Voronoibased methods and well-known algorithms that use the crust structure for curve reconstruction is introduced
Summary
Samples of shape boundary carry the information of the shape for a sufficiently dense sampling. Algorithms that use Delaunay triangulation and Voronoi diagram of sample points for crust and skeleton approximation can be used just for closed curves. Amenta et al (Amenta et al, 1998) proposed a Voronoi-based algorithm (called crust algorithm) to reconstruct the boundary from a set of sample points forming the boundary of a shape. In this algorithm, the crust is a subset of the edges of the Delaunay triangulation of the sample points. The crust algorithm is based on the fact that an edge e of the DT belongs to the crust if e has a circum-circle that contains neither sample points nor Voronoi vertices of S. D1D2 belongs to the crust if this determinant is negative, otherwise V1V2 belongs to the skeleton
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
