Advertisement

3D-based reconstruction using growing neural gas landmark: application to rapid prototyping in shoe last manufacturing

  • Antonio Jimeno-Morenilla
  • Jose García-Rodriguez
  • Sergio Orts-Escolano
  • Miguel Davia-Aracil
ORIGINAL ARTICLE

Abstract

Customizing shoe manufacturing is one of the great challenges in the footwear industry. It is a production model change where design adopts not only the main role, but also the main bottleneck. It is therefore necessary to accelerate this process by improving the accuracy of current methods. Rapid prototyping techniques are based on the reuse of manufactured footwear lasts so that they can be modified with CAD systems leading rapidly to new shoe models. In this work, we present a shoe last fast reconstruction method that fits current design and manufacturing processes. The method is based on the scanning of shoe last obtaining sections and establishing a fixed number of landmarks onto those sections to reconstruct the shoe last 3D surface. Automated landmark extraction is accomplished through the use of the self-organizing network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates up to 12 times the surface reconstruction and filtering processes used by the current shoe last design software. The proposed method offers higher accuracy compared with methods with similar efficiency as voxel grid.

Keywords

Shoe manufacturing Shoe last rapid prototyping 3D surface reconstruction Landmarking Growing neural gas Voxel grid 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wang CS (2010) An analysis and evaluation of fitness for shoe last and human feet. J Comput Industry 61(6):532–540CrossRefGoogle Scholar
  2. 2.
    Jimeno-Morenilla A, López V, Espi R, Cuenca S (2011) A morphological-based method for inverse offset generation. An application for surface reconstruction using mechanical digitizers. Int J Adv Manuf Technol 54(9):1067–1076CrossRefGoogle Scholar
  3. 3.
    Edelsbrunner H, Mucke EP (1994) Three dimensional alpha shapes. ACM Trans Graphics (TOG) 13(1):43–42CrossRefzbMATHGoogle Scholar
  4. 4.
    Boissonnat JD (1984) Geometric structures for three-dimensional shape representation. ACM Trans Graphics (TOG) 3(4):266–286CrossRefGoogle Scholar
  5. 5.
    Amenta N, Choi S, Kolluri RK (2001) The power crust. Proceedings of the sixth ACM symposium on Solid modeling and applications (SMA '01). ACM, pp. 249–266Google Scholar
  6. 6.
    Dey TK, Goswami S (2003) Tight Cocone: a water-tight surface reconstructor. Proceedings of the eighth ACM symposium on Solid modeling and applications (SM '03). ACM, pp. 127–134Google Scholar
  7. 7.
    Dey TK, Goswami S (2004) Provable surface reconstruction from noisy samples. In Proc. 20th ACM Sympos. Comput. GeomGoogle Scholar
  8. 8.
    Mederos B, Amenta N, Velho L and De Figueiredo L.E (2005) Surface reconstruction form noisy point clouds. Proceedings of the third Eurographics symposium on Geometry processing. Eurographics Association, pp. 53Google Scholar
  9. 9.
    Lorensen WE, Cline HE (1987) Marching cubes: a high resolution 3D surface construction algorithm. Proceedings of the 14th annual conference on Computer graphics and interactive techniques (SIGGRAPH '87), 21(4):163–169Google Scholar
  10. 10.
    Hoppe H (1994) Surface reconstruction from unorganized points. Ph.D. Dissertation, University of WashingtonGoogle Scholar
  11. 11.
    Carr JC, Beatson RK, Cherrie JB, Mitchell TJ, Fright WR, McCallum BC, Evans TR (2001) Reconstruction and representation of 3D objects with radial basic functions. Proceedings of the 28th annual conference on Computer graphics and interactive techniques (SIGGRAPH '01). ACM, pp. 67–76Google Scholar
  12. 12.
    Shen C, O'Brien JF, Shewchuk JR (2004) Interpolating and approximating implicit surfaces from polygon soup. ACM Trans Graphics (TOG) 23(3):896–904CrossRefGoogle Scholar
  13. 13.
    Fleishman S, Cohen-Or D, Silva CT (2005) Robust moving least squares fitting with sharp features. ACM Trans Graphics (TOG) 24(3):544–552CrossRefGoogle Scholar
  14. 14.
    Walder C, Schoelkopf B, Chapelle O (2006) Implicit surface modelling with a globally regularised basis of compact support. Proc Eur Symp Comput Graphics Eur Assoc 25(3):635–644Google Scholar
  15. 15.
    Kazhdan M, Bolitho M, Hoppe H (2006) Poisson surface reconstruction. Proceedings of the fourth Eurographics symposium on Geometry processing (SGP '06). Eurographics Association, pp. 61–70Google Scholar
  16. 16.
    Wang CL (2006) Incremental reconstruction of sharp edges on mesh surfaces. J Computer-aided design 38(6):789–702Google Scholar
  17. 17.
    Connolly CI (1984) Cumulative generation of octrees models from range data. In Proceedings, Intl. Conf. Robotics, pages 25–32Google Scholar
  18. 18.
    Kobbelt L, Botsch M (2004) A survey of point based techniques in computer graphics. Comput Graph 28(6):801–814CrossRefGoogle Scholar
  19. 19.
    Jimeno Morenilla A, García Chamizo JM, Salas F (2001) Shoe last machining using virtual digitizing. Int J Adv Manuf Technol 17:744–750CrossRefGoogle Scholar
  20. 20.
    Davies HR, Twining JC, Cootes FT, Waterton CJ, Taylor JC (2002) A minimum description length approach to statistical shape modeling. IEEE Trans Med Imaging 21(5):525–537CrossRefGoogle Scholar
  21. 21.
    Fatemizadeh E, Lucas C, Soltania-Zadeh H (2003) Automatic landmark extraction from image data using modified growing neural gas network. IEEE Trans Inf Technol Biomed 7(2):77–85CrossRefGoogle Scholar
  22. 22.
    Ansari N, Delp EJ (1991) On detecting dominant points. Pattern Recognit 26:441–451CrossRefGoogle Scholar
  23. 23.
    The C-H, Chin RT (1989) On the detection of dominant points on digital curves. IEEE Trans Pattern Anal Machine Intell 11(8):859–872CrossRefGoogle Scholar
  24. 24.
    Pei S-C, Lin C-N (1992) The detection of dominant points on digital curves by scale-space filtering. Pattern Recognit 25:1307–1314CrossRefGoogle Scholar
  25. 25.
    Fritzke B (1995) A growing neural gas network learns topologies. In: Tesauro G, Touretzky DS, Leen TK (eds) Advances in neural information processing systems 7. MIT Press, Cambridge, pp 625–632Google Scholar
  26. 26.
    Martinetz T, Shulten K (1994) Topology representing networks. Neural Networks 7(3):507–522CrossRefGoogle Scholar
  27. 27.
    Garcia-Rodriguez J, Angelopoulou A, Psarrou A (2006) Growing neural gas (GNG): a soft competitive learning method for 2D hand modelling. IEICE Trans Inf & Syst E89-D(7):2124–2131CrossRefGoogle Scholar
  28. 28.
    Angelopoulou A, Psarrou A, Garcia-Rodriguez J, Revett K (2005) Automatic landmarking of 2D medical shapes using the growing neural gas network. In Proc. Of the IEEE Workshop on Computer Vision for Biomedical Image Applications, CVBIA 2005, LNCS 3765, pp. 210–219Google Scholar
  29. 29.
    Rusu RB, Cousins S (2011) 3D is here: point cloud library (PCL). In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Shanghai, ChinaGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Antonio Jimeno-Morenilla
    • 1
  • Jose García-Rodriguez
    • 1
  • Sergio Orts-Escolano
    • 1
  • Miguel Davia-Aracil
    • 1
  1. 1.Department of Computer TechnologyUniversity of AlicanteAlicanteSpain

Personalised recommendations