Cluster Computing

, Volume 22, Supplement 4, pp 9069–9080 | Cite as

Fast global illumination of dynamic water surface based on two stage rendering

  • Hua LiEmail author
  • Huamin Yang
  • Chao Xu
  • Jianping Zhao


Water simulation plays an important role in computer graphics, and its complex optical properties are very computationally intensive due to the variation of phenomena. In this paper, a fast-global illumination method of dynamic water surface with precise reflection and refraction is proposed using a two-stage rendering strategy. At the first stage, we generate an improved Secondary Texture Map (iSDT) based on the distortion of Perlin noise. Our method generates octaves of Perlin noise to construct random height of water surface. For fast rendering the reflection, we utilize 2D mesh modelling water surfaces and optimize the uniform mesh of surface by a layer of detail approach. A 3D geometries’ mirror reflection with respect to 3D perspective view is computed and stored as a texture map. Then the texture map is distorted by octaves of Perlin noise. At the second stage, we combine the iSDT with ray tracing strategy for computing global illumination above the water surface. Experimental results show that our method reduces the rendering time compared with the original ray tracing, and both the opaque and transparent geometries are rendered with plausible high-quality with correct reflection and refraction.


Water simulation Perlin noise Distorted texture Global illumination 



This work was supported by Natural Science Foundation of Jilin of China (20170101005JC) and partial supported by Jilin province major science and technology bidding project (20170203004GX), Jilin province industrial innovation special funds projects (2016C091), Key Science and Technology Project of Jilin Province (20160204019GX).


  1. 1.
    Génevaux, J.D., Galin, É., Guérin, E., et al.: Terrain generation using procedural models based on hydrology [J]. ACM Trans. Gr. (TOG) 32(4), 143 (2013)zbMATHGoogle Scholar
  2. 2.
    Lee, M.W., Jung, C.H., Lee, M.G., et al.: Data definition of 3D character modelling and animation using H-Anim[J]. JoC 6(2), 19–29 (2015)Google Scholar
  3. 3.
    Monaghan, J.J.: Smoothed particle hydrodynamics [J]. Ann. Rev. Astron. Astrophys. 30(1), 543–574 (1992)CrossRefGoogle Scholar
  4. 4.
    Huang, C., Zhu, J., Sun, H., et al.: Parallel-optimizing SPH fluid simulation for realistic VR environments [J]. Comput. Anim. Virtual Worlds 26(1), 43–54 (2015)CrossRefGoogle Scholar
  5. 5.
    Chan, K.H., Ke, W., Im, S.K.: Particle–mesh coupling in the interaction of fluid and deformable bodies with screen space refraction rendering [J]. Comput. Anim. Virtual Worlds (2017). CrossRefGoogle Scholar
  6. 6.
    Im, S.K., Chan, K.H.: Fast particle neighbor searching for unlimited scene with fluid refraction improvement [J]. Int. J. Model. Optim. 6(2), 71 (2016)CrossRefGoogle Scholar
  7. 7.
    Djado, K., Egli, R., Granger, F.: Particle-based drop animation on meshes in real time[J]. Comput. Anim. Virtual Worlds 23(3–4), 301–309 (2012)CrossRefGoogle Scholar
  8. 8.
    Liu, S., Xiong, Y.: Fast and stable simulation of virtual water scenes with interactions. Virtual Real. 17, 77 (2013)CrossRefGoogle Scholar
  9. 9.
    Wang, Y., Baboulin, M., Dongarra, J., et al.: A parallel solver for incompressible fluid flows [J]. Procedia Comput. Sci. 18, 439–448 (2013)CrossRefGoogle Scholar
  10. 10.
    Liu, S., Xiong, Y.: Fast and stable simulation of virtual water scenes with interactions [J]. Virtual Real. 17(1), 77–88 (2013)CrossRefGoogle Scholar
  11. 11.
    Macklin, M., Müller, M., Chentanez, N., et al.: Unified particle physics for real-time applications [J]. ACM Trans. Gr. (TOG) 33(4), 153 (2014)Google Scholar
  12. 12.
    Darles, E., Crespin, B., Ghazanfarpour, D., et al.: A survey of ocean simulation and rendering techniques in computer graphics. Comput. Gr. Forum 30(1), 43–60 (2011)CrossRefGoogle Scholar
  13. 13.
    Nielsen, M.B., Söderström, A., Bridson, R.: Synthesizing waves from animated height fields[J]. ACM Trans. Gr. (TOG) 32(1), 2 (2013)zbMATHGoogle Scholar
  14. 14.
    Lee, H.M., Go, C., Lee, W.H.: An efficient algorithm for rendering large bodies of water [J]. Entertain. Comput. ICEC 2006, 302–305 (2006)Google Scholar
  15. 15.
    Bruneton, E., Neyret, F., Holzschuch, N.: Real-time realistic ocean lighting using seamless transitions from geometry to BRDF. Comput. Gr. Forum 29(2), 487–496 (2010)CrossRefGoogle Scholar
  16. 16.
    Simulation, I.H.: View-dependent tessellation and simulation of ocean surfaces [J]. Sci. World J. 3, 979418 (2014)Google Scholar
  17. 17.
    Saravanan, V., Pralhaddas, K.D., Kothari, D.P., et al.: An optimizing pipeline stall reduction algorithm for power and performance on multi-core CPUs [J]. Hum. Centric Comput. Inf. Sci. 5(1), 2 (2015)CrossRefGoogle Scholar
  18. 18.
    Liang, J., Gong, J., Li, Y.: Realistic rendering for physically based shallow water simulation in virtual geographic environments (VGEs) [J]. Ann GIS 21(4), 301–312 (2015)CrossRefGoogle Scholar
  19. 19.
    Smelik, R.M., Tutenel, T., Bidarra, R., et al.: A survey on procedural modelling for virtual worlds. Comput. Gr. Forum 33(6), 31–50 (2014)CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyChangchun University of Science and TechnologyChangchunChina

Personalised recommendations