Triangle mesh-based edge detection and its application to surface segmentation and adaptive surface smoothing

Triangle meshes are widely used in representing surfaces in computer vision and computer graphics. Although 2D image processing-based edge detection techniques have been popular in many application areas, they are not well developed for surfaces represented by triangle meshes. This paper proposes a robust edge detection algorithm for triangle meshes and its applications to surface segmentation and adaptive surface smoothing. The proposed edge detection technique is based on eigen analysis of the surface normal vector field in a geodesic window. To compute the edge strength of a certain vertex, the neighboring vertices in a specified geodesic distance are involved. Edge information are used further to segment the surfaces with the watershed algorithm and to achieve edge-preserved, adaptive surface smoothing. The proposed algorithm is novel in robustly detecting edges on triangle meshes against noise. The 3D watershed algorithm is an extension from previous work. Experimental results on surfaces reconstructed from multi-view real range images are presented.

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

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

[3]  Mi-Suen Lee,et al.  A Computational Framework for Segmentation and Grouping , 2000 .

[4]  Gabriel Taubin,et al.  A signal processing approach to fair surface design , 1995, SIGGRAPH.

[5]  J A Sethian,et al.  Computing geodesic paths on manifolds. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Yutaka Ohtake,et al.  Polyhedral surface smoothing with simultaneous mesh regularization , 2000, Proceedings Geometric Modeling and Processing 2000. Theory and Applications.

[7]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid , 2012 .

[8]  Jos B. T. M. Roerdink,et al.  The Watershed Transform: Definitions, Algorithms and Parallelization Strategies , 2000, Fundam. Informaticae.

[9]  Mongi A. Abidi,et al.  Surface matching by 3D point's fingerprint , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[10]  Alex M. Andrew,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science (2nd edition) , 2000 .

[11]  Joonki Paik,et al.  Robust crease detection and curvature estimation of piecewise smooth surfaces from triangle mesh approximations using normal voting , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.