Surface Reconstruction of Plant Shoots from Multiple Views

  • Michael P. PoundEmail author
  • Andrew P. French
  • Erik H. Murchie
  • Tony P. Pridmore
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8928)


Increased adoption of the systems approach to biological research has focused attention on the use of quantitative models of biological objects. This includes a need for realistic 3D representations of plant shoots for quantification and modelling. We present a fully automatic approach to image-based 3D plant reconstruction. The reconstructed plants are represented as a series of small planar sections that together model the more complex architecture of the leaf surfaces. The boundary of each leaf patch is refined using the level set method, optimising the model based on image information, curvature constraints and the position of neighbouring surfaces. The reconstruction process makes few assumptions about the nature of the plant material being reconstructed, and as such is applicable to a wide variety of plant species and topologies, and can be extended to canopy-scale imaging. We demonstrate the effectiveness of our approach on datasets of wheat and rice plants, as well as a novel virtual dataset that allows us to compute distance measures of reconstruction accuracy.


Plant phenotyping Multi-view reconstruction 3D Level sets 


  1. 1.
    Quan, L., Tan, P., Zeng, G., Yuan, L., Wang, J., Kang, S.B.: Image-based plant modeling. ACM Transactions on Graphics 25(3), 599–604 (2006)CrossRefGoogle Scholar
  2. 2.
    Watanabe, T., Hanan, J.S., Room, P.M., Hasegawa, T., Nakagawa, H., Takahashi, W.: Rice morphogenesis and plant architecture: measurement, specification and the reconstruction of structural development by 3D architectural modelling. Annals of Botany 95(7), 1131–1143 (2005)CrossRefGoogle Scholar
  3. 3.
    Alarcon, V.J., Sassenrath, G.F.: Modelling cotton (Gossypium spp.) leaves and canopy using computer aided geometric design (CAGD). Ecological Modelling 222(12), 1951–1963 (2011)CrossRefGoogle Scholar
  4. 4.
    Houle, D., Govindaraju, D.R., Omholt, S.: Phenomics: the next challenge. Nature Reviews Genetics 11(12), 855–866 (2010)CrossRefGoogle Scholar
  5. 5.
    White, J.W., Andrade-Sanchez, P., Gore, M.A., Bronson, K.F., Coffelt, T.A., Conley, M.M., Feldmann, K.A.: Field-based phenomics for plant genetics research. Field Crops Research 133, 101–112 (2012)CrossRefGoogle Scholar
  6. 6.
    Ma, W., Zha, H., Liu, J., Zhang, X., Xiang, B.: Image-based plant modeling by knowing leaves from their apexes. In: 19th International Conference on Pattern Recognition, pp. 1–4 (2008)Google Scholar
  7. 7.
    Alenya, G., Dellen, B., Torras, C.: 3D modelling of leaves from color and ToF data for robotized plant measuring. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3408–3414 (2011)Google Scholar
  8. 8.
    Clark, R.T., MacCurdy, R.B., Jung, J.K., Shaff, J.E., McCouch, S.R., Aneshansley, D.J., Kochian, L.V.: Three-dimensional root phenotyping with a novel imaging and software platform. Plant Physiology 156(2), 455–465 (2011)CrossRefGoogle Scholar
  9. 9.
    Kumar, P., Cai, J., Miklavcic, S.: High-throughput 3D modelling of plants for phenotypic analysis. In: Proceedings of the 27th Conference on Image and Vision Computing New Zealand, pp. 301–306 (2012)Google Scholar
  10. 10.
    Kutulakos, K.N., Seitz, S.M.: A theory of shape by space carving. International Journal of Computer Vision 38(3), 199–218 (2000)CrossRefzbMATHGoogle Scholar
  11. 11.
    Cai, J., Miklavcic, S.: Automated extraction of three-dimensional cereal plant structures from two-dimensional orthographic images. Image Processing 6(6), 687–696 (2012)CrossRefGoogle Scholar
  12. 12.
    Qingfeng, S., Guilian, Z., Xin-Guang, Z.: Optimal crop canopy architecture to maximise canopy photosynthetic CO2 uptake under elevated CO2 – a theoretical study using a mechanistic model of canopy photosynthesis. Functional Plant Biology 40, 108–124 (2013)CrossRefGoogle Scholar
  13. 13.
    Furukawa, Y., Ponce, J.: Accurate, dense, and robust multiview stereopsis. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(8), 1362–1376 (2010)CrossRefGoogle Scholar
  14. 14.
    Wu, C.: VisualSFM: A visual structure from motion system (2011)Google Scholar
  15. 15.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)Google Scholar
  16. 16.
    Hartley, R., Zisserman, A.: Multiple View Geometry in computer vision. Cambridge University Press (2003)Google Scholar
  17. 17.
    Carr, J.C., Beatson, R.K., Cherrie, J.B., Mitchell, T.J., Fright, W.R., McCallum, B.C., Evans, T.R.: Reconstruction and representation of 3D objects with radial basis functions. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 67–76 (2001)Google Scholar
  18. 18.
    Kazhdan, M., Bolitho, M., Hoppe, H.: Poisson surface reconstruction. In: Proceedings of the Fourth Eurographics Symposium on Geometry Processing (2006)Google Scholar
  19. 19.
    Klasing, K., Wollherr, D., Buss, M.: A clustering method for efficient segmentation of 3D laser data. In: International Conference on Robotics and Automation (ICRA), pp. 4043–4048 (2008)Google Scholar
  20. 20.
    Edelsbrunner, H., Kirkpatrick, D.G., Raimund, S.: On the shape of a set of points in the plane. IEEE Transactions on Information Theory 29(4), 551–559 (1983)CrossRefzbMATHGoogle Scholar
  21. 21.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988)CrossRefGoogle Scholar
  22. 22.
    Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics 79(1), 12–49 (1988)CrossRefzbMATHMathSciNetGoogle Scholar
  23. 23.
    Sethian, J.A.: Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science. Cambridge University Press 3 (1999)Google Scholar
  24. 24.
    Rosin, P.L.: Unimodal thresholding. Pattern Recognition 34(11), 2083–2096 (2001)CrossRefzbMATHGoogle Scholar
  25. 25.
    Shewchuk, J.R.: Delaunay Refinement Algorithms for Triangular Mesh Generation. Computational Geometry: Theory and Applications 22(1–3), 21–74 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  26. 26.
    SC Pixelmachine SRL: Topogun, v2.0.
  27. 27.
    Blender Foundation: Blender, v2.69.
  28. 28.
    Cignoni, P., Rocchini, C., Scopigno, R.: Metro: Measuring error on simplified surfaces. Computer Graphics Forum 17(2), 167–174 (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Michael P. Pound
    • 1
    Email author
  • Andrew P. French
    • 1
    • 2
  • Erik H. Murchie
    • 1
  • Tony P. Pridmore
    • 1
    • 2
  1. 1.Centre for Plant Integrative BiologyUniversity of NottinghamNottinghamUK
  2. 2.School of Computer ScienceUniversity of NottinghamNottinghamUK

Personalised recommendations