Skip to main content
SpringerLink
Log in

An efficient and robust algorithm for 3D mesh segmentation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper presents an efficient and robust algorithm for 3D mesh segmentation. Segmentation is one of the main areas of 3D object modeling. Most segmentation methods decompose 3D objects into parts based on curvature analysis. Most of the existing curvature estimation algorithms are computationally costly. The proposed algorithm extracts features using Gaussian curvature and concaveness estimation to partition a 3D model into meaningful parts. More importantly, this algorithm can process highly detailed objects using an eXtended Multi-Ring (XMR) neighborhood based feature extraction. After feature extraction, we also developed a fast marching watershed-based segmentation algorithm followed by an efficient region merging scheme. Experimental results show that this segmentation algorithm is efficient and robust.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Barr AH (1981, Jan) Superquadric and angle-preserving transformation. IEEE Trans Comput Graph Appl 1:11–23

    Article  Google Scholar 

  2. Beucher S, Lantuéjoul C (1979, Sept 17–21) Use of watershed in contour detection. In Proc. international workshop on image processing, Renes

  3. Chen L, Georganas ND (2002, Nov) An efficient multi-resolution modeling for 3D objects in applications of virtual environment. HAVE2002, pp. 49–54, Ottawa

  4. Chevalier L, Jaillet F, Baskurt A (2003, Feb) Segmentation and superquadric modeling of 3D objects. Journal of WSCG 11(1). WSCG2003

    Google Scholar 

  5. Garland M, Willmott A, Heckbert PS (2001, March) Hierarchical face clustering on polygon surfaces. ACM Symposium on Interactive 3D Graphics, pp. 49–58

  6. Gray A (1993) The Gaussian and mean curvatures. §14.5 in modern differential geometry of curves and surfaces. CRC, Boca Raton, Florida, pp 279–285

    Google Scholar 

  7. Hoppe H (1996, Aug) Progressive meshes. SIGGRAPH 96 conference proceedings, pp 99–108

  8. Isler V, Lau RWH, Green M (1996, Jul) Real-time multi-resolution modeling for complex environments. Symposium on virtual reality software and technology, pp 11–19

  9. Leonardis A, Jaklic A, Solina F (1997, Nov) Superquadrics for segmentation and modeling range data. IEEE Trans Pattern Anal Mach Intell 19:1289–1295

    Article  Google Scholar 

  10. Li J, Kuo, C-CJ (1997, Oct) Embedding coding of 3D graphic models. ICIP’97, Vol. 1, pp 57–60

  11. Mangan A, Whitaker R (1999, Oct.–Dec) Partitioning 3D surface meshes using watershed segmentation. IEEE Trans Vis Comput Graph 5(4):308–321

    Article  Google Scholar 

  12. Millman RS, Parker GD (1977) Elements of Differential Geometry, Prentice-Hall Inc

  13. Pulla S, Razdan A, Farin G (2002) Improved curvature estimation for watershed segmentation of 3-dimensional Meshes. Submitted to IEEE Trans Vis Comput Graph

  14. Razdan A, Bae M (2002, Apr) A hybrid approach to feature segmentation of 3-dimensional meshes. Computer-Aided Design

  15. Roerdlink JR, Meijster A (2001) The watershed transform: definitions, algorithms and parallelization strategies. Fundamenta Informaticae 41:187–228

    Google Scholar 

  16. Rossl C, Kobbelt L, Seidel H-P (2000) Extraction of feature lines on triangulated surfaces usingmorphological operators. Smart Graphics, AAAI Spring Symposium, Stanford University, pp 71–75

  17. Schroeder W, Zarge J, Lorensen W (1992, July) Decimation of triangle meshes. Computer Graphics 25(3):65–70 (Proc. SIGGRAPH '92)

    Google Scholar 

  18. Vivodtzev F, Linsen L, Bonneau G-P, Hamann B, Joy KI, Olshausen BA (2003) Hierarchical isosurface segmentation based on discrete curvature. Proc. of VisSym '03, Eurographics—IEEE TVCG symposium on visualization

  19. Wu K, Levine MD (1997, Nov) 3D part segmentation using simulated electrical charge distributions. IEEE Trans Pattern Anal Mach Intell 19:1223–1235

    Article  Google Scholar 

  20. Zhang Y, Paik JK, Koschan A, Abidi MA, Gorsich D (2002, Sep) A simple and efficient algorithm for part decomposition of 3D triangulated models based on curvature analysis. Proc. int. conf. image processing, rochester, New York, Vol. III, pp. 273–276

  21. Rettmann ME, Han X, Xu C, Prince JL (2002) Automated sulcal segmentation using watersheds on the cortical surface. NeuroImage (15):329–344

Download references

Author information

Authors and Affiliations

  1. School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, K1N 6N5, Canada

    Lijun Chen & Nicolas D. Georganas

Authors
  1. Lijun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicolas D. Georganas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lijun Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, L., Georganas, N.D. An efficient and robust algorithm for 3D mesh segmentation. Multimed Tools Appl 29, 109–125 (2006). https://doi.org/10.1007/s11042-006-0002-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-006-0002-x

Keywords

Search

Navigation