3D-NCuts: Adapting Normalized Cuts to 3D Triangulated Surface Segmentation

Zahra Toony, Denis Laurendeau, Philippe Giguère, Christian Gagné

2014

Abstract

Being able to automatically segment 3D models into meaningful parts is an important goal in 3D shape processing. In this paper, we are proposing a fast and easy-to-implement 3D segmentation approach, which is based on spectral clustering. For this purpose, we define an improved formulation of the similarity matrix which allows our algorithm to segment both free-form and CAD (Computer Aided Design) 3D models. In 3D space, different shapes, such as planes and cylinders, have different surface normal distributions. We defined the similarity of vertices based on their normals which can segment a 3D model into its geometric features. Results show the effectiveness and robustness of our method in segmenting a wide range of 3D models. Even in the case of complex models, our method results in meaningful segmentations. We tested our segmentation approach on real data segmentation, in the presence of noise and also in comparison with other methods which provided good results in all cases.

References

  1. Agathos, A., Pratikakis, I., Perantonis, S., Sapidis, N., and Azariadis, P. (2007). 3D mesh segmentation methodologies for CAD applications. Computer-Aided Design and Applications, 4(6):827-841.
  2. Attene, M., Falcidieno, B., and Spagnuolo, M. (2006). Hierarchical mesh segmentation based on fitting primitives. The Visual Computer, 22(3):181-193.
  3. Benhabiles, H., Lavoué, G., Vandeborre, J.-P., and Daoudi, M. (2011). Learning boundary edges for 3D-Mesh segmentation. In Computer Graphics Forum, volume 30, pages 2170-2182. Wiley Online Library.
  4. Benhabiles, H., Lavoué, G., Vandeborre, J.-P., Daoudi, M., et al. (2012). Kinematic skeleton extraction based on motion boundaries for 3D dynamic meshes. In Eurographics 2012 Workshop on 3D Object Retrieval, pages 71-76.
  5. Benhabiles, H., Vandeborre, J.-P., Lavoué, G., and Daoudi, M. (2009). A framework for the objective evaluation of segmentation algorithms using a ground-truth of human segmented 3D-models. In IEEE International Conf. on Shape Modeling and Applications (SMI), pages 36-43.
  6. Blais, F. (2004). Review of 20 years of range sensor development. volume 13, pages 231-243. Journal of Electronic Imaging.
  7. Carnero, J., Molina-Abril, H., and Real, P. (2012). Triangle mesh compression and homological spanning forests. In Proceedings of the 4th international conf. on Computational Topology in Image Context, pages 108-116.
  8. Chen, X., Golovinskiy, A., and Funkhouser, T. (2009). A benchmark for 3D mesh segmentation. ACM Transactions on Graphics (TOG), 28(3):73.
  9. Chung, F. R. (1997). Spectral graph theory. CBMS Regional Conference Series in Mathematics, 92.
  10. Fowlkes, C., Belongie, S., Chung, F., and Malik, J. (2004). Spectral grouping using the Nystrom method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(2):214-225.
  11. Golovinskiy, A. and Funkhouser, T. (2008). Randomized cuts for 3D mesh analysis. In ACM Transactions on Graphics (TOG), volume 27, page 145.
  12. Huber, D., Kapuria, A., Donamukkala, R., and Hebert, M. (2004). Parts-based 3D object classification. In Computer Vision and Pattern Recognition (CVPR), volume 2, pages 82-89.
  13. Jayanti, S., Kalyanaraman, Y., Iyer, N., and Ramani, K. (2006). Developing an engineering shape benchmark for CAD models. Computer-Aided Design, 38(9):939-953.
  14. Jiang, W., Tian, J., Cai, K., Zhang, F., and Luo, T. (2012). Tangent-plane-continuity maximization based 3D point compression. In 19th IEEE International Conf. on Image Processing (ICIP), pages 1277-1280.
  15. Kalogerakis, E., Hertzmann, A., and Singh, K. (2010). Learning 3D mesh segmentation and labeling. ACM Transactions on Graphics (TOG), 29(4):102.
  16. Karim Baareh, A., Sheta, A. F., and Al-Batah, M. S. (2012). Feature based 3D object recognition using artificial neural networks. International Journal of Computer Applications, 44(5):1-7.
  17. Katz, S. and Tal, A. (2003). Hierarchical mesh decomposition using fuzzy clustering and cuts, volume 22. ACM Trans. on Graphics.
  18. Lai, Y.-K., Hu, S.-M., Martin, R. R., and Rosin, P. L. (2008). Fast mesh segmentation using random walks. In Proceedings of the ACM symposium on Solid and physical modeling, pages 183-191.
  19. Lavoué, G., Vandeborre, J.-P., Benhabiles, H., Daoudi, M., Huebner, K., Mortara, M., Spagnuolo, M., et al. (2012). SHREC'12 Track: 3D mesh segmentation. In Eurographics 2012 Workshop on 3D Object Retrieval, pages 93-99.
  20. Liu, R. and Zhang, H. (2004). Segmentation of 3D meshes through spectral clustering. In Proceedings. 12th Pacific Conf. on Computer Graphics and Applications, pages 298-305.
  21. Liu, R. and Zhang, H. (2007). Mesh segmentation via spectral embedding and contour analysis. In Computer Graphics Forum, volume 26, pages 385-394. Wiley Online Library.
  22. Murase, H. and Nayar, S. K. (1995). Visual learning and recognition of 3-D objects from appearance. International journal of computer vision, 14(1):5-24.
  23. Ning, X., Li, E., Zhang, X., and Wang, Y. (2010). Shape decomposition and understanding of point cloud objects based on perceptual information. In Proceedings of the 9th ACM SIGGRAPH Conf. on VirtualReality Continuum and its Applications in Industry, pages 199-206.
  24. Sam, V., Kawata, H., and Kanai, T. (2012). A robust and centered curve skeleton extraction from 3D point cloud. Computer-Aided Design & Applications, 9(6):869-879.
  25. Selinger, A. and Nelson, R. C. (1999). A perceptual grouping hierarchy for appearance-based 3D object recognition. Computer Vision and Image Understanding, 76(1):83-92.
  26. Shamir, A. (2008). A survey on mesh segmentation techniques. In Computer graphics forum, volume 27, pages 1539-1556. Wiley Online Library.
  27. Shapira, L., Shamir, A., and Cohen-Or, D. (2008). Consistent mesh partitioning and skeletonisation using the shape diameter function. The Visual Computer, 24(4):249-259.
  28. Shi, J. and Malik, J. (2000). Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888-905.
  29. Shlafman, S., Tal, A., and Katz, S. (2002). Metamorphosis of polyhedral surfaces using decomposition. In Computer Graphics Forum, volume 21, pages 219- 228. Wiley Online Library.
  30. Xiao, D., Lin, H., Xian, C., and Gao, S. (2011). CAD mesh model segmentation by clustering. Computers & Graphics, 35(3):685-691.
  31. Yu, H., Chen, J., Wan, W., Wang, R., and Yu, X. (2012). A segmentation progressive mesh compression method. In International Conf. on Audio, Language and Image Processing (ICALIP), pages 1163-1166.
  32. Zhang, H. and Liu, R. (2005). Mesh segmentation via recursive and visually salient spectral cuts. In Proc. of vision, modeling, and visualization, pages 429-436.
  33. Zhang, Q., Song, X., Shao, X., Shibasaki, R., and Zhao, H. (2012). Unsupervised skeleton extraction and motion capture from 3D point cloud sequences. Neurocomputing, 100.
  34. Zhang, Y., Paik, J., Koschan, A., Abidi, M. A., and Gorsich, D. (2002). Simple and efficient algorithm for part decomposition of 3-D triangulated models based on curvature analysis. In International Conference on Image Processing, volume 3, pages III-273.
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Paper Citation


in Harvard Style

Toony Z., Laurendeau D., Giguère P. and Gagné C. (2014). 3D-NCuts: Adapting Normalized Cuts to 3D Triangulated Surface Segmentation . In Proceedings of the 9th International Conference on Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2014) ISBN 978-989-758-002-4, pages 144-152. DOI: 10.5220/0004692101440152


in Bibtex Style

@conference{grapp14,
author={Zahra Toony and Denis Laurendeau and Philippe Giguère and Christian Gagné},
title={3D-NCuts: Adapting Normalized Cuts to 3D Triangulated Surface Segmentation},
booktitle={Proceedings of the 9th International Conference on Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2014)},
year={2014},
pages={144-152},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004692101440152},
isbn={978-989-758-002-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2014)
TI - 3D-NCuts: Adapting Normalized Cuts to 3D Triangulated Surface Segmentation
SN - 978-989-758-002-4
AU - Toony Z.
AU - Laurendeau D.
AU - Giguère P.
AU - Gagné C.
PY - 2014
SP - 144
EP - 152
DO - 10.5220/0004692101440152