Multimedia Tools and Applications

, Volume 77, Issue 20, pp 27143–27162 | Cite as

A new semantic segmentation approach of 3D mesh using the stereoscopic image colors

  • Abderazzak TaimeEmail author
  • Abderrahim Saaidi
  • Khalid Satori


This paper introduces a new mesh segmentation approach into semantic parts, most closely resemble those made by humans, which is based on the pixel color of the images used in the 3D reconstruction. This approach allows to segment the mesh into semantic and a much simpler way than most of the mesh segmentation methods that are based on the geometrical characteristics of the mesh. The principle of our method is to establish a link between the color objects of the scene and the mesh while exploiting the link between the interest points of the images brought into play and the vertices of 3D mesh. The results in great part, reflect the efficiency and performance of our method.


Mesh segmentation Semantic parts Pixel color Link Interest points 


  1. 1.
    Attene M, Falcidieno B, Spagnuolo M (2006) Hierarchical mesh segmentation based on fitting primitives. Vis Comput 22(3):181–193CrossRefGoogle Scholar
  2. 2.
    Bhatia SK (2004) Adaptive k-means clustering. In FLAIRS conference (pp. 695–699)Google Scholar
  3. 3.
    Cazals F, Giesen J (2006) Delaunay triangulation based surface reconstruction. In Effective computational geometry for curves and surfaces(pp. 231–276). Springer Berlin HeidelbergGoogle Scholar
  4. 4.
    Chen X, Golovinskiy A, Funkhouser T (2009) A benchmark for 3D mesh segmentation. In ACM Transactions on Graphics (TOG) (Vol. 28, No. 3, p. 73). ACMCrossRefGoogle Scholar
  5. 5.
    Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395MathSciNetCrossRefGoogle Scholar
  6. 6.
    El Akkad N, Merras M, Saaidi A, Satori K (2014) Camera self-calibration with varying intrinsic parameters by an unknown three-dimensional scene. Vis Comput 30(5):519–530CrossRefGoogle Scholar
  7. 7.
    Fu KS (Ed.) (2013) VLSI for pattern recognition and image processing (Vol. 13). Springer Science & Business MediaGoogle Scholar
  8. 8.
    Furukawa Y, Ponce J (2010) Accurate, dense, and robust multiview stereopsis. IEEE Trans Pattern Anal Mach Intell 32(8):1362–1376CrossRefGoogle Scholar
  9. 9.
    George D, Xie X, Tam GK (2017) 3D Mesh Segmentation via Multi-branch 1D Convolutional Neural Networks. arXiv preprint arXiv:1705.11050Google Scholar
  10. 10.
    Ghosh M, Amato NM, Lu Y, Lien JM (2013) Fast approximate convex decomposition using relative concavity. Computer-Aided DesignGoogle Scholar
  11. 11.
    Goesele M, Snavely N, Curless B, Hoppe H, Seitz SM (2007). Multi-view stereo for community photo collections. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on (pp. 1–8). IEEEGoogle Scholar
  12. 12.
    Gold CM, Dakowicz M (2005) The crust and skeleton–applications in GIS. In Proceedings, 2nd. International Symposium on Voronoi Diagrams in Science and Engineering (pp. 33–42)Google Scholar
  13. 13.
    Golovinskiy A, Funkhouser T (2008) Randomized cuts for 3D mesh analysis. ACM transactions on graphics (TOG) 27(5):145CrossRefGoogle Scholar
  14. 14.
    Golovinskiy A, Funkhouser T (2008). Randomized cuts for 3D mesh analysis. In ACM transactions on graphics (TOG) (Vol. 27, No. 5, p. 145). ACMCrossRefGoogle Scholar
  15. 15.
    Jeon J, Jung Y, Kim H, Lee S (2016) Texture map generation for 3D reconstructed scenes. The Visual Computer, 1–11Google Scholar
  16. 16.
    Katz S, Tal A (2003) Hierarchical mesh decomposition using fuzzy clustering and cuts. ACM Trans. Graph. (SIGGRAPH) 22(3):954–961CrossRefGoogle Scholar
  17. 17.
    Katz S, Leifman G, Tal A (2005) Mesh segmentation using feature point and core extraction. Vis Comput 21(8–10):649–658CrossRefGoogle Scholar
  18. 18.
    Krayevoy V, Sheffer A (2006). Variational, meaningful shape decomposition. In ACM SIGGRAPH 2006 Sketches (p. 50). ACMGoogle Scholar
  19. 19.
    Lai P, Samson C (2016) Applications of mesh parameterization and deformation for unwrapping 3D images of rock tunnels. Tunn Undergr Space Technol 58:109–119CrossRefGoogle Scholar
  20. 20.
    Lai YK, Hu SM, Martin RR, Rosin PL (2008). Fast mesh segmentation using random walks. In Proceedings of the 2008 ACM symposium on Solid and physical modeling (pp. 183–191). ACMGoogle Scholar
  21. 21.
    Lhuillier M, Quan L (2002) Match propagation for image-based modeling and rendering. IEEE Trans Pattern Anal Mach Intell 24(8):1140–1146CrossRefGoogle Scholar
  22. 22.
    Lhuillier M, Quan L (2005) A quasi-dense approach to surface reconstruction from uncalibrated images. IEEE Trans Pattern Anal Mach Intell 27(3):418–433CrossRefGoogle Scholar
  23. 23.
    Lien JM, Amato NM (2007) Approximate convex decomposition of polyhedra. In Proceedings of the 2007 ACM symposium on Solid and physical modeling (pp. 121–131). ACMGoogle Scholar
  24. 24.
    Lien JM, Amato NM (2008) Approximate convex decomposition of polyhedra and its applications. Computer Aided Geometric Design 25(7):503–522MathSciNetCrossRefGoogle Scholar
  25. 25.
    Liu R, Zhang H, Busby J (2008). Convex hull covering of polygonal scenes for accurate collision detection in games. In Proceedings of graphics interface 2008 (pp. 203–210). Canadian Information Processing SocietyGoogle Scholar
  26. 26.
    Lowe DG (1999) Object recognition from local scale-invariant features. In Computer vision, 1999. The proceedings of the seventh IEEE international conference on (Vol. 2, pp. 1150–1157). IeeeGoogle Scholar
  27. 27.
    Pollefeys M, Koch R, Vergauwen M, Van Gool L (2000) Automated reconstruction of 3D scenes from sequences of images. ISPRS J Photogramm Remote Sens 55(4):251–267CrossRefGoogle Scholar
  28. 28.
    Serino L, di Baja GS, Arcelli C (2010) Object decomposition via curvilinear skeleton partition. In 2010 International Conference on Pattern Recognition (pp. 4081–4084). IEEEGoogle Scholar
  29. 29.
    Shapira L, Shamir A, Cohen-Or D (2008) Consistent mesh partitioning and skeletonisation using the shape diameter function. Vis Comput 24(4):249–259CrossRefGoogle Scholar
  30. 30.
    Shu Z, Qi C, Xin S, Hu C, Wang L, Zhang Y, Liu L (2016) Unsupervised 3D shape segmentation and co-segmentation via deep learning. Computer Aided Geometric Design 43:39–52MathSciNetCrossRefGoogle Scholar
  31. 31.
    Ulusoy AO, Black MJ, Geiger A (2017) Semantic multi-view stereo: Jointly estimating objects and voxels. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (Vol. 2)Google Scholar
  32. 32.
    Wang H, Lu T, Au OKC, Tai CL (2014) Spectral 3D mesh segmentation with a novel single segmentation field. Graph Model 76(5):440–456CrossRefGoogle Scholar
  33. 33.
    Xie Z, Xu K, Liu L, Xiong Y (2014) 3d shape segmentation and labeling via extreme learning machine. In Computer graphics forum (Vol. 33, No. 5, pp. 85–95)CrossRefGoogle Scholar
  34. 34.
    Yao L, Huang S, Xu H, Li P (2015) Quadratic error metric mesh simplification algorithm based on discrete curvature. Math Probl Eng 2015:1–7MathSciNetzbMATHGoogle Scholar
  35. 35.
    Zeramdini B, Robert C, Germain G, Pottier T (2016) Simulation of Metal Forming Processes with a 3D Adaptive Remeshing ProcedureGoogle Scholar
  36. 36.
    Zöckler M, Stalling D, Hege HC (2000) Fast and intuitive generation of geometric shape transitions. Vis Comput 16(5):241–253CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Abderazzak Taime
    • 1
    Email author
  • Abderrahim Saaidi
    • 1
    • 2
  • Khalid Satori
    • 1
  1. 1.LIIAN, Department of Mathematics and Computer Science Faculty of Sciences Dhar-MahrezSidi Mohamed Ben Abdellah UniversityFezMorocco
  2. 2.LSI, Department of Mathematics, Physics and Computer Science Polydisciplinary Faculty of TazaSidi Mohamed Ben Abdellah UniversityTazaMorocco

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