SIAM Journal on Computing
Least squares conformal maps for automatic texture atlas generation
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Computer Aided Geometric Design
Estimating differential quantities using polynomial fitting of osculating jets
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Ridge-valley lines on meshes via implicit surface fitting
ACM SIGGRAPH 2004 Papers
Fast and robust detection of crest lines on meshes
Proceedings of the 2005 ACM symposium on Solid and physical modeling
Fast exact and approximate geodesics on meshes
ACM SIGGRAPH 2005 Papers
A Sampling Framework for Accurate Curvature Estimation in Discrete Surfaces
IEEE Transactions on Visualization and Computer Graphics
Smooth feature lines on surface meshes
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
Describing shapes by geometrical-topological properties of real functions
ACM Computing Surveys (CSUR)
Feature detection of triangular meshes based on tensor voting theory
Computer-Aided Design
Stability of curvature measures
SGP '09 Proceedings of the Symposium on Geometry Processing
Separatrix persistence: extraction of salient edges on surfaces using topological methods
SGP '09 Proceedings of the Symposium on Geometry Processing
Selection of an optimal polyhedral surface model using the minimum description length principle
Proceedings of the 32nd DAGM conference on Pattern recognition
Markov random fields for improving 3D mesh analysis and segmentation
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
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Recent advances in 3D reconstruction allow to quickly acquire highly detailed and complex geometry. However, the outcome of such systems is usually unstructured, noisy and redundant. In order to enable further processing such as CAD modeling, physical measurement or rendering, semantic information about shape and topology needs to be derived from the data. In this paper, a robust approach to the extraction of a feature skeleton is presented. The skeleton reflects the overall structure of an object. It is given by a set of lines that run along ridges or valleys and meet at umbilical points. The computed data is not just useful for building semantic-driven CAD models in reverse engineering disciplines but also to identify geometrical features for tasks like object recognition, registration, rendering or re-meshing. Based on the mean curvature, a Markov random field is used to robustly classify each vertex either belonging to convex, concave or flat regions. The boundaries of the regions are described by a set of points that are robustly estimated using linear interpolation. A novel algorithm is used to extract the feature skeleton based on the Voronoi decomposition of the boundary points. The method has been successfully tested on real world examples and the paper concludes with a detailed evaluation.