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Multi-patch B-Spline Statistical Shape Models for CAD-Compatible Digital Human Modeling

  • Toon HuysmansEmail author
  • Femke Danckaers
  • Jochen Vleugels
  • Daniël Lacko
  • Guido De Bruyne
  • Stijn Verwulgen
  • Jan Sijbers
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 780)

Abstract

Parametric 3D human body models are valuable tools for ergonomic product design and statistical shape modelling (SSM) is a powerful technique to build realistic body models from a database of 3D scans. Like the underlying 3D scans, body models built from SSMs are typically represented with triangle meshes. Unfortunately, triangle meshes are not well supported by CAD software where spline geometry dominates. Therefore, we propose a methodology to convert databases of pre-corresponded triangle meshes into multi-patch B-spline SSMs. An evaluation on four 3D scan databases shows that our method is able to generate accurate and water-tight models while preserving inter-subject correspondences by construction. In addition, we demonstrate that such SSMs can be used to generate design manikins which can be readily used in SolidWorks for designing well conforming product parts.

Keywords

Statistical shape modeling B-splines Computer-aided design Digital human modeling 

Notes

Acknowledgments

This work was financially supported by VLAIO grants TETRA-130771 and SB-141520.

References

  1. 1.
    Pheasant, S., Haslegrave, C.M.: Bodyspace: Anthropometry, Ergonomics and the Design of Work. CRC Press, Boca Raton (2016)Google Scholar
  2. 2.
    Motti, V.G., Caine, K.: Human factors considerations in the design of wearable devices. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 58, no. 1, pp. 1820–1824. SAGE Publications, Thousand Oaks (2014)Google Scholar
  3. 3.
    Baek, S.Y., Lee, K.: Parametric human body shape modeling framework for human-centered product design. Comput.-Aided Des. 44(1), 56–67 (2012)CrossRefGoogle Scholar
  4. 4.
    Heimann, T., Meinzer, H.P.: Statistical shape models for 3D medical image segmentation: a review. Med. Image Anal. 13(4), 543–563 (2009)CrossRefGoogle Scholar
  5. 5.
    Davies, R.H., Twining, C.J., Cootes, T.F., Taylor, C.J.: Building 3-D statistical shape models by direct optimization. IEEE Trans. Med. Imaging 29(4), 961–981 (2010)CrossRefGoogle Scholar
  6. 6.
    Huysmans, T., Sijbers, J., Verdonk, B.: Automatic construction of correspondences for tubular surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 32(4), 636–651 (2010)CrossRefGoogle Scholar
  7. 7.
    Allen, B., Curless, B., Popović, Z.: The space of human body shapes: reconstruction and parameterization from range scans. ACM Trans. Graph. 22(3), 587–594 (2003)CrossRefGoogle Scholar
  8. 8.
    Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., Davis, J.: SCAPE: shape completion and animation of people. ACM Trans. Graph. 24(3), 408–416 (2005)CrossRefGoogle Scholar
  9. 9.
    Hasler, N., Stoll, C., Sunkel, M., Rosenhahn, B., Seidel, H.P.: A statistical model of human pose and body shape. Comput. Graph. Forum 28(2), 337–346 (2009)CrossRefGoogle Scholar
  10. 10.
    Chen, Y., Liu, Z., Zhang, Z.: Tensor-based human body modeling. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 105–112. IEEE (2013)Google Scholar
  11. 11.
    Park, B.K., Reed, M.P.: Parametric body shape model of standing children aged 3–11 years. Ergonomics 58(10), 1714–1725 (2015)CrossRefGoogle Scholar
  12. 12.
    Pishchulin, L., Wuhrer, S., Helten, T., Theobalt, C., Schiele, B.: Building statistical shape spaces for 3D human modeling. Pattern Recogn. 67, 276–286 (2017)CrossRefGoogle Scholar
  13. 13.
    Quan, W., Matuszewski, B., Shark, L., Ait-Boudaoud, D.: 3-D facial expression representation using B-spline statistical shape model. In: British Machine Vision Conference, Vision, Video and Graphics Workshop, 10–13 September 2007, Warwick (2007)Google Scholar
  14. 14.
    Hu, N., Cerviño, L., Segars, P., Lewis, J., Shan, J., Jiang, S., Wang, G.: A method for generating large datasets of organ geometries for radiotherapy treatment planning studies. Radiol. Oncol. 48(4), 408–415 (2014)CrossRefGoogle Scholar
  15. 15.
    Peng, W., Feng, Z., Xu, C., Su, Y.: Parametric T-spline face morphable model for detailed fitting in shape subspace. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6139–6147 (2017)Google Scholar
  16. 16.
    Campen, M.: Partitioning surfaces into quadrilateral patches: a survey. Comput. Graph. Forum 36(8), 567–588 (2017)CrossRefGoogle Scholar
  17. 17.
    Bommes, D., Zimmer, H., Kobbelt, L.: Mixed-integer quadrangulation. ACM Trans. Graph. 28(3), 77:1–77:10 (2009)Google Scholar
  18. 18.
    Kimmel, R., Sethian, J.A.: Computing geodesic paths on manifolds. Proc. Natl. Acad. Sci. 95(15), 8431–8435 (1998)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Floater, M.S.: Parametrization and smooth approximation of surface triangulations. Comput. Aided Geom. Des. 14(3), 231–250 (1997)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Floater, M.S.: Mean value coordinates. Comput. Aided Geom. Des. 20(1), 19–27 (2003)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Bartels, R.H., Beatty, J.C., Barsky, B.A.: An Introduction to Splines for Use in Computer Graphics and Geometric Modeling. Morgan Kaufmann, Burlington (1987)Google Scholar
  22. 22.
    Li, X.S.: An overview of SuperLU: algorithms, implementation, and user interface. ACM Trans. Math. Softw. 31(3), 302–325 (2005)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Capetillo-Cunliffe, L.: Loni: Laboratory of Neuro Imaging (2007)Google Scholar
  24. 24.
    Robinette, K.M., Daanen, H., Paquet, E.: The CAESAR project: a 3-D surface anthropometry survey. In: Second IEEE International Conference on 3-D Digital Imaging and Modeling, pp. 380–386 (1999)Google Scholar
  25. 25.
    Danckaers, F., Huysmans, T., Lacko, D., Ledda, A., Verwulgen, S., Van Dongen, S., Sijbers, J.: Correspondence preserving elastic surface registration with shape model prior. In: 22nd International Conference on Pattern Recognition (ICPR), pp. 2143–2148. IEEE (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Toon Huysmans
    • 1
    • 2
    Email author
  • Femke Danckaers
    • 2
  • Jochen Vleugels
    • 3
  • Daniël Lacko
    • 3
  • Guido De Bruyne
    • 3
  • Stijn Verwulgen
    • 3
  • Jan Sijbers
    • 2
  1. 1.Section on Applied Ergonomics and Design, Faculty of Industrial Design EngineeringDelft University of TechnologyDelftThe Netherlands
  2. 2.imec - Vision Lab, Department of Physics, Faculty of ScienceUniversity of AntwerpAntwerpBelgium
  3. 3.Department of Product Development, Faculty of Design SciencesUniversity of AntwerpAntwerpBelgium

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