Surface mesh segmentation and smooth surface extraction through region growing

Laser range-scanners are used in fields as diverse as product design, reverse engineering, and rapid prototyping to quickly acquire geometric surface data of parts and models. This data is often in the form of a dense, noisy surface mesh that must be simplified into piecewise-smooth surfaces. The method presented here facilitates this time-consuming task by automatically segmenting a dense mesh into regions closely approximated by single surfaces. The algorithm first estimates the noise and curvature of each vertex. Then it filters the curvatures and partitions the mesh into regions with fundamentally different shape characteristics. These regions are then contracted to create seed regions for region growing. For each seed region, the algorithm iterates between region growing and surface fitting to maximize the number of connected vertices approximated by a single underlying surface. The algorithm finishes by filling segment holes caused by outlier noise. We demonstrate the algorithm effectiveness on real data sets.

[1]  Anshuman Razdan,et al.  A hybrid approach to feature segmentation of triangle meshes , 2003, Comput. Aided Des..

[2]  Ralph R. Martin,et al.  Methods to recover constant radius rolling ball blends in reverse engineering , 1999, Comput. Aided Geom. Des..

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

[4]  Gene H. Golub,et al.  Matrix Computations, Third Edition , 1996 .

[5]  Chandra Kambhamettu,et al.  A Novel Method for 3D Surface Mesh Segmentation , 2003, Computer Graphics and Imaging.

[6]  Tamás Várady,et al.  Segmentation methods for smooth point regions of conventional engineering objects , 2004, Comput. Aided Des..

[7]  Mahmoud Melkemi,et al.  Range-Image segmentation and model reconstruction based on a fit-and-merge strategy , 2002, SMA '02.

[8]  Martin D. Levine,et al.  3D part segmentation using simulated electrical charge distributions , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[9]  Ralph R. Martin,et al.  Algorithms for reverse engineering boundary representation models , 2001, Comput. Aided Des..

[10]  Min Yang,et al.  Segmentation of measured point data using a parametric quadric surface approximation , 1999, Comput. Aided Des..

[11]  Gábor Renner,et al.  Advanced surface fitting techniques , 2002, Comput. Aided Geom. Des..

[12]  G. Farin.  Curves and Surfaces for Cagd: A Practical Guide , 2001 .

[13]  Hans-Peter Seidel,et al.  Proceedings of the seventh ACM symposium on Solid modeling and applications , 2002 .

[14]  Ralph R. Martin,et al.  Robust Segmentation of Primitives from Range Data in the Presence of Geometric Degeneracy , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Sylvain Petitjean,et al.  A survey of methods for recovering quadrics in triangle meshes , 2002, CSUR.

[16]  Kai Hormann,et al.  Parameterization of Triangulations and Unorganized Points , 2002, Tutorials on Multiresolution in Geometric Modelling.

[17]  Franc Solina,et al.  Superquadrics for Segmenting and Modeling Range Data , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Gene H. Golub,et al.  Matrix computations , 1983 .

[19]  Paul J. Besl,et al.  Direct construction of polynomial surfaces from dense range images through region growing , 1995, TOGS.

[20]  Cecil L. Smith,et al.  Digital control of industrial processes , 1970, CSUR.

[21]  Weiyin Ma,et al.  Parameterization of randomly measured points for least squares fitting of B-spline curves and surfaces , 1995, Comput. Aided Des..

[22]  Mark Meyer,et al.  Discrete Differential-Geometry Operators for Triangulated 2-Manifolds , 2002, VisMath.

[23]  William H. Press,et al.  Numerical recipes in C , 2002 .

[24]  Les A. Piegl,et al.  The NURBS Book , 1995, Monographs in Visual Communication.

[25]  Michael S. Floater,et al.  Parametrization and smooth approximation of surface triangulations , 1997, Comput. Aided Geom. Des..

[26]  Ramesh C. Jain,et al.  Segmentation through Variable-Order Surface Fitting , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Leif Kobbelt,et al.  Extraction of feature lines on triangulated surfaces using morphological operators , 2000 .

[28]  Kenji Shimada,et al.  Smoothing of Noisy Laser Scanner Generated Meshes Using Polynomial Fitting and Neighborhood Erosion , 2003, DAC 2003.

[29]  M. Abidi,et al.  Part decomposition of 3d surfaces , 2003 .

[30]  Paul J. Besl,et al.  Surfaces in Range Image Understanding , 1988, Springer Series in Perception Engineering.

[31]  Lawrence O'Gorman,et al.  Practical Algorithms for Image Analysis: Description, Examples and Code , 2000 .

[32]  Alan M. McIvor,et al.  A comparison of local surface geometry estimation methods , 1997, Machine Vision and Applications.

[33]  Chia-Hsiang Menq,et al.  Automatic data segmentation for geometric feature extraction from unorganized 3-D coordinate points , 2001, IEEE Trans. Robotics Autom..

[34]  Bernd Hamann.  Visualization and modeling contours of trivariate functions , 1991 .

[35]  Gerald Farin,et al.  Curves and surfaces for cagd , 1992 .