Convenient Tree Species Modeling for Virtual Cities

  • Like GobeawanEmail author
  • Daniel Joseph Wise
  • Alex Thiam Koon Yee
  • Sum Thai Wong
  • Chi Wan Lim
  • Ervine Shengwei Lin
  • Yi Su
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11542)


Generating large scale 3D tree models for digital twin cities at a species level-of-detail poses challenges of automation and maintenance of such dynamically evolving models. This paper presents an inverse procedural modeling methodology to automate the generation of 3D tree species models based on growth spaces from point clouds and pre-formulated L-system growth rules. The rules capture the botanical tree architecture at a species level in terms of growth process, branching pattern, and responses to external stimuli. Users only need to fill in a species profile template and provide the growth space derivable from the point clouds. The parameters involved in the rules are automatically optimised within the growth space to produce the species models to represent actual trees. This methodology enables users without 3D modeling skills to conveniently produce highly representative 3D models of any tree species in a large scale.


Tree fractal models Procedural modeling L-system Tree species profile Tree architecture Optimisation 



This work is supported by National Research Foundation Singapore, Virtual Singapore Award no. NRF2015VSG-AA3DCM001-034. Authors thank colleagues at IHPC (A*STAR), NParks, and GovTech for their valuable input and support.


  1. 1.
    Barthélémy, D., Caraglio, Y.: Plant architecture: a dynamic, multilevel and comprehensive approach to plant form, structure and ontogeny. Ann. Bot. 99, 375–407 (2007)CrossRefGoogle Scholar
  2. 2.
    Bernard, J., McQuillan, I.: A fast and reliable hybrid approach for inferring l-systems. In: The 2018 Conference on Artificial Life: A Hybrid of the European Conference on Artificial Life (ECAL) and the International Conference on the Synthesis and Simulation of Living Systems (ALIFE), vol. 30, pp. 444–451 (2018)Google Scholar
  3. 3.
    Borchert, R., Honda, H.: Control of development in the bifurcating branch system of tabebuia rosea: a computer simulation. Bot. Gaz. 145(2), 184–195 (1984)CrossRefGoogle Scholar
  4. 4.
    Boudon, F., Pradal, C., Cokelaer, T., Prusinkiewicz, P., Godin, C.: L-Py: an l-system simulation framework for modeling plant architecture development based on a dynamic language. Front. Plant Sci. 3, 76 (2012)CrossRefGoogle Scholar
  5. 5.
    Paine, C.E.T., et al.: How to fit nonlinear plant growth models and calculate growth rates: an update for ecologists. Methods Ecol. Evol. 3, 245–256 (2012)CrossRefGoogle Scholar
  6. 6.
    Ge, Z., Poh, H.J., Wise, D.J., Lim, C.W., Gobeawan, L., Lou, J.: Drag force prediction with CFD full closure model simulation on scaled fractal tree in wind tunnel. In: Proceedings of The Eighth International Symposium on Physics of Fluids (2019)Google Scholar
  7. 7.
    Gobeawan, L., et al.: Modeling trees for virtual Singapore: from data acquisition to cityGML models. Int. Arch. Photogrammetry Rem. Sens. Spat. Inf. Sci. XLII-4/W10, 55–62 (2018)CrossRefGoogle Scholar
  8. 8.
    Godin, C., Sinoquet, H.: Functionalstructural plant modelling. New Phytol. 166(3), 705–708 (2005)CrossRefGoogle Scholar
  9. 9.
    Hallé, F., Oldeman, R.A.A., Tomlinson, P.B.: Tropical Trees and Forests: An Architectural Analysis. Springer, New York (1978)CrossRefGoogle Scholar
  10. 10.
    Hu, S., Li, Z., Zhang, Z., He, D., Wimmer, M.: Efficient tree modeling from airborne LiDAR point clouds. Comput. Graph. 67(C), 1–13 (2017)Google Scholar
  11. 11.
    Kamal, M., Phinn, S., Johansen, K.: Object-based approach for multi-scale mangrove composition mapping using multi-resolution image datasets. Rem. Sens. 7(4), 4753–4783 (2015)CrossRefGoogle Scholar
  12. 12.
    Kang, M., Hua, J., De Reffye, P., Jaeger, M.: Parameter identification of plant growth models with stochastic development, pp. 98–105, November 2016Google Scholar
  13. 13.
    Kass, R.E., Carlin, B.P., Gelman, A., Neal, R.M.: Markov chain monte carlo in practice: a roundtable discussion. Am. Statist. 52(2), 93–100 (1998)MathSciNetGoogle Scholar
  14. 14.
    Lin, E.S., Teo, L.S., Yee, A.T.K., Li, Q.H.: Populating large scale virtual city models with 3D trees. In: 55th IFLA (International Federation of Landscape Architects) World Congress (2018)Google Scholar
  15. 15.
    Lindenmayer, A.: Mathematical models for cellular interactions in development I. Filaments with one-sided inputs. J. Theor. Biol. 18(3), 280–299 (1968)CrossRefGoogle Scholar
  16. 16.
    Lintermann, B., Deussen, O.: Interactive modeling of plants. IEEE Comput. Graph. Appl. 19(1), 56–65 (1999)CrossRefGoogle Scholar
  17. 17.
    Livny, Y., et al.: Texture-lobes for tree modelling. ACM Trans. Graph. 30(4), 53:1–53:10 (2011)CrossRefGoogle Scholar
  18. 18.
    Longay, S., Runions, A., Boudon, F., Prusinkiewicz, P.: Treesketch: interactive procedural modeling of trees on a tablet. In: Kara, L., Singh, K. (eds.) EUROGRAPHICS Symposium on Sketch-Based Interfaces and Modeling, Cagliari, Italy (2012)Google Scholar
  19. 19.
    McKay, M.D.: Latin hypercube sampling as a tool in uncertainty analysis of computer models. In: Proceedings of the 24th Conference on Winter Simulation, WSC 1992, pp. 557–564. ACM, New York (1992)Google Scholar
  20. 20.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  21. 21.
    Prusinkiewicz, P., Lindenmayer, A.: The Algorithmic Beauty of Plants. Springer, Berlin (1996)zbMATHGoogle Scholar
  22. 22.
    Reche-Martinez, A., Martin, I., Drettakis, G.: Volumetric reconstruction and interactive rendering of trees from photographs. ACM Trans. Graph. 23(3), 720–727 (2004)CrossRefGoogle Scholar
  23. 23.
    Sievänen, R., Godin, C., Dejong, T., Nikinmaa, E.: Functional-structural plant models: a growing paradigm for plant studies. Ann. Bot. 114, 599–603, September 2014CrossRefGoogle Scholar
  24. 24.
    Stava, O., et al.: Inverse procedural modelling of trees. Comput. Graph. Forum 33(6), 118–131 (2014)CrossRefGoogle Scholar
  25. 25.
    Vos, J., Evers, J.B., Buck-Sorlin, G.H., Andrieu, B., Chelle, M., de Visser, P.H.B.: Functional structural plant modelling: a new versatile tool in crop science. J. Exp. Bot. 61(8), 2101–2115 (2010)CrossRefGoogle Scholar
  26. 26.
    Weber, J., Penn, J.: Creation and rendering of realistic trees. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1995, pp. 119–128. ACM, New York (1995)Google Scholar
  27. 27.
    Xu, H., Gossett, N., Chen, B.: Knowledge and heuristic-based modeling of laser-scanned trees. ACM Trans. Graph. 26(4), 19 (2007)CrossRefGoogle Scholar
  28. 28.
    Xu, L., Mould, D.: Procedural tree modeling with guiding vectors. Comput. Graph. Forum 34(7), 47–56 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of High Performance Computing, A*STARSingaporeSingapore
  2. 2.National Parks BoardSingaporeSingapore

Personalised recommendations