A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Partitioning 3D Surface Meshes Using Watershed Segmentation
IEEE Transactions on Visualization and Computer Graphics
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Molecular shape analysis based upon the morse-smale complex and the connolly function
Proceedings of the nineteenth annual symposium on Computational geometry
Feature Sensitive Mesh Segmentation with Mean Shift
SMI '05 Proceedings of the International Conference on Shape Modeling and Applications 2005
Hierarchical mesh segmentation based on fitting primitives
The Visual Computer: International Journal of Computer Graphics
Mesh Segmentation - A Comparative Study
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal curvatures from the integral invariant viewpoint
Computer Aided Geometric Design
Fast mesh segmentation using random walks
Proceedings of the 2008 ACM symposium on Solid and physical modeling
Automatic recognition of features from freeform surface CAD models
Computer-Aided Design
Integral invariants for robust geometry processing
Computer Aided Geometric Design
Rapid and effective segmentation of 3D models using random walks
Computer Aided Geometric Design
A new CAD mesh segmentation method, based on curvature tensor analysis
Computer-Aided Design
3D mesh segmentation using mean-shifted curvature
GMP'08 Proceedings of the 5th international conference on Advances in geometric modeling and processing
Feature suppression based CAD mesh model simplification
Computer-Aided Design
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
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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.