CAD mesh model segmentation by clustering

CAD mesh models have been widely employed in current CAD/CAM systems, where it is quite useful to recognize the features of the CAD mesh models. The first step of feature recognition is to segment the CAD mesh model into meaningful parts. Although there are lots of mesh segmentation methods in literature, the majority of them are not suitable to CAD mesh models. In this paper, we design a mesh segmentation method based on clustering, dedicated to the CAD mesh model. Specifically, by the agglomerative clustering method, the given CAD mesh model is first clustered into the sparse and dense triangle regions. Furthermore, the sparse triangle region is separated into planar regions, cylindrical regions, and conical regions by the Gauss map of the triangular faces and Hough transformation; the dense triangle region is also segmented by the mean shift operation performed on the mean curvature field defined on the mesh faces. Lots of empirical results demonstrate the effectiveness and efficiency of the CAD mesh segmentation method in this paper.

[1]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Marco Attene,et al.  Mesh Segmentation - A Comparative Study , 2006, IEEE International Conference on Shape Modeling and Applications 2006 (SMI'06).

[3]  Andreas Nüchter,et al.  A Data Structure for the 3D Hough Transform for Plane Detection , 2010 .

[4]  Ralph R. Martin,et al.  Fast mesh segmentation using random walks , 2008, SPM '08.

[5]  Xi Zhang,et al.  3D Mesh Segmentation Using Mean-Shifted Curvature , 2008, GMP.

[6]  Erkki Oja,et al.  A new curve detection method: Randomized Hough transform (RHT) , 1990, Pattern Recognit. Lett..

[7]  Ross T. Whitaker,et al.  Partitioning 3D Surface Meshes Using Watershed Segmentation , 1999, IEEE Trans. Vis. Comput. Graph..

[8]  Xiang Chen,et al.  Feature suppression based CAD mesh model simplification , 2008, Comput. Aided Des..

[9]  Ariel Shamir,et al.  A survey on Mesh Segmentation Techniques , 2008, Comput. Graph. Forum.

[10]  S. S. Pande,et al.  Automatic recognition of features from freeform surface CAD models , 2008, Comput. Aided Des..

[11]  Shi-Min Hu,et al.  Principal curvatures from the integral invariant viewpoint , 2007, Comput. Aided Geom. Des..

[12]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Frédéric Chazal,et al.  Molecular shape analysis based upon the morse-smale complex and the connolly function , 2002, SCG '03.

[14]  Leonidas J. Guibas,et al.  Persistence-based segmentation of deformable shapes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[15]  Atilla Baskurt,et al.  A new CAD mesh segmentation method, based on curvature tensor analysis , 2005, Comput. Aided Des..

[16]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[17]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[18]  Johannes Wallner,et al.  Integral invariants for robust geometry processing , 2009, Comput. Aided Geom. Des..

[19]  Leo Grady,et al.  Random Walks for Image Segmentation , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Hans-Peter Seidel,et al.  Feature sensitive mesh segmentation with mean shift , 2005, International Conference on Shape Modeling and Applications 2005 (SMI' 05).

[21]  Ralph R. Martin,et al.  Rapid and effective segmentation of 3D models using random walks , 2009, Comput. Aided Geom. Des..

[22]  P. Green,et al.  Analyzing multivariate data , 1978 .

[23]  Marco Attene,et al.  Hierarchical mesh segmentation based on fitting primitives , 2006, The Visual Computer.