An improved contraction-based method for mesh skeleton extraction

Contraction-based skeleton extraction methods have the feature that during skeleton extraction process, the correspondence between skeleton and mesh regions can be obtained, which makes this class of algorithm attractive. Besides, among all mesh skeleton extraction methods, contraction-based methods possesses the merits of robustness to noise, rotation invariant and no requirement on additional boundary conditions. However, contraction-based methods still suffer some flaws such as not promising homotopy or centeredness, or not capable of processing non-closed meshes, etc. In this paper, an improved contraction-based skeleton extraction method is proposed which covers the failure of existing methods at non-closed part of a model and increases the rationality of the centeredness correction of the skeleton: First, non-closed models are virtually closed by a preprocessing stage such that models with boundaries can be contracted in the same way as the closed ones. Second, to improve the centeredness of the skeleton, we present a simpler and more effective one-ring area sequence weighting scheme by which the displacements measuring the shift of skeleton nodes can be calculated. Experimental results show the effectiveness of our work.

[1]  Deborah Silver,et al.  Curve-Skeleton Properties, Applications, and Algorithms , 2007, IEEE Trans. Vis. Comput. Graph..

[2]  Tamal K. Dey,et al.  Defining and computing curve-skeletons with medial geodesic function , 2006, SGP '06.

[3]  Junjie Cao,et al.  Point Cloud Skeletons via Laplacian Based Contraction , 2010, 2010 Shape Modeling International Conference.

[4]  Wan-Chun Ma,et al.  Skeleton extraction of 3D objects with radial basis functions , 2003, 2003 Shape Modeling International..

[5]  Attila Kuba,et al.  A Parallel 3D 12-Subiteration Thinning Algorithm , 1999, Graph. Model. Image Process..

[6]  Tong-Yee Lee,et al.  Curve-Skeleton Extraction Using Iterative Least Squares Optimization , 2008, IEEE Transactions on Visualization and Computer Graphics.

[7]  Tong-Yee Lee,et al.  Skeleton extraction by mesh contraction , 2008, SIGGRAPH 2008.

[8]  Markus Ilg,et al.  Voronoi skeletons: theory and applications , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Natapon Pantuwong,et al.  Skeleton-growing: a vector-field-based 3D curve-skeleton extraction algorithm , 2010, SA '10.

[10]  Taku Komura,et al.  Topology matching for fully automatic similarity estimation of 3D shapes , 2001, SIGGRAPH.

[11]  Christine Depraz,et al.  Harmonic skeleton for realistic character animation , 2007, SCA '07.

[12]  Marco Attene,et al.  Shape understanding by contour-driven retiling , 2003, The Visual Computer.

[13]  Jiann-Der Lee,et al.  Three-Dimensional Topology Preserving Reduction on the 4-Subfields , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Frederick W. B. Li,et al.  Feature-varying skeletonization , 2012, The Visual Computer.

[15]  Gabriella Sanniti di Baja,et al.  Distance-Driven Skeletonization in Voxel Images , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Balasubramanian Raman,et al.  Computing hierarchical curve-skeletons of 3D objects , 2005, The Visual Computer.

[17]  Deborah Silver,et al.  Curve-skeletons: properties, computation and applications , 2007 .

[18]  Thomas A. Funkhouser,et al.  The Princeton Shape Benchmark , 2004, Proceedings Shape Modeling Applications, 2004..