Image-Based Post-processing for Realistic Real-Time Rendering of Scenes in the Presence of Fluid Simulations and Image-Based Lighting

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10072)

Abstract

For real-time fluid simulation currently two methods are available: grid-based simulation and particle-based simulation. They both approximate the simulation of a fluid and have in common that they do not directly generate a visually pleasant surface. Due to time constraints, the subsequent generation of the fluid surface may not consume much time. What is usually generated is an approximate surface, which consists of many individual mesh elements and has no optical properties of a fluid. The visualization of a fluid in image space may contain different detail densities depending on the distance between observer and the fluid. Therefore, filters need to be applied in order to smooth these details to a consistent surface. Many approaches use strong filters in this step, which results in a too smooth surface. To this surface then noise is added in order to give it a rough appearance. To avoid this ad-hoc approach we present a post-processing approach of the direct visualization of the simulation data via image processing applications by both smoothing filters and an image pyramid. Our presented approach based on an image pyramid provides access to various levels of detail. These are used as a controllable low pass filter. Thus, different amounts of smoothing can be selected depending on the distance to the viewer, granting a better surface reconstruction.

References

  1. 1.
    Chentanez, N., Müller, M., Kim, T.: Coupling 3D eulerian, heightfield and particle methods for interactive simulation of large scale liquid phenomena. IEEE Trans. Vis. Comput. Graph. 21, 1116–1128 (2015)CrossRefGoogle Scholar
  2. 2.
    Liu, W., Ribeiro, E.: A higher-order model for fluid motion estimation. In: Kamel, M., Campilho, A. (eds.) ICIAR 2011, Part I. LNCS, vol. 6753, pp. 325–334. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-21593-3_33 CrossRefGoogle Scholar
  3. 3.
    Bender, J., Müller, M., Otaduy, M.A., Teschner, M., Macklin, M.: A survey on position-based simulation methods in computer graphics. Comput. Graph. Forum 33, 228–251 (2014)CrossRefGoogle Scholar
  4. 4.
    Puhl, J.: Materialsysteme für das realistische echtzeit-rendering von szenen in anwesenheit von flüssigkeitssimulationen und image-based lighting. Technical report, TU Darmstadt (2014)Google Scholar
  5. 5.
    van der Laan, W.J., Green, S., Sainz, M.: Screen space fluid rendering with curvature flow. In: Proceedings of the 2009 Symposium on Interactive 3D Graphics, SI3D 2009, 27 February – 1 March, 2009, Boston, Massachusetts, USA, pp. 91–98 (2009)Google Scholar
  6. 6.
    Müller, M., Schirm, S., Duthaler, S.: Screen space meshes. In: Proceedings of the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2007, pp. 9–15. Eurographics Association, Aire-la-Ville (2007)Google Scholar
  7. 7.
    Macklin, M., Müller, M.: Position based fluids. ACM Trans. Graph. 32, 104:1–104:12 (2013)CrossRefMATHGoogle Scholar
  8. 8.
    Yu, J., Turk, G.: Reconstructing surfaces of particle-based fluids using anisotropic kernels. ACM Trans. Graph. 32, 5 (2013)CrossRefMATHGoogle Scholar
  9. 9.
    Goswami, P., Schlegel, P., Solenthaler, B., Pajarola, R.: Interactive SPH simulation and rendering on the GPU. In: Proceedings of the 2010 Eurographics/ACM SIGGRAPH Symposium on Computer Animation, SCA 2010, pp. 55–64 (2010)Google Scholar
  10. 10.
    Kornprobst, P., Tumblin, J., Durand, F.: Bilateral filtering: theory and applications. Found. Trends Comput. Graph. Vis. 4, 1–74 (2009)MATHGoogle Scholar
  11. 11.
    Wong, T.: Image-based lighting. In: Ikeuchi, K. (ed.) Computer Vision, A Reference Guide, pp. 387–390. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  12. 12.
    Knuth, M., Altenhofen, C., Kuijper, A., Bender, J.: Efficient self-shadowing using image-based lighting on glossy surfaces. In: Vision, Modeling and Visualization, VMV 2014, pp. 159–166 (2014)Google Scholar
  13. 13.
    Reinhard, E., Ward, G., Pattanaik, S.N., Debevec, P.E., Heidrich, W.: High Dynamic Range Imaging - Acquisition, Display, and Image-Based Lighting, 2nd edn. Academic Press, Orlando (2010)Google Scholar
  14. 14.
    Shreiner, D., Sellers, G., Kessenich, J., Licea-Kaneand, B.: OpenGL Programming Guide: The Official Guide to Learning OpenGL, Versions 4.3, 8th edn. Addison-Wesley Professional, Upper Saddle River (2013)Google Scholar
  15. 15.
    Akenine-Möller, T., Haines, E., Hoffman, N.: Real-Time Rendering, 3rd edn. A.K. Peters Ltd., Natick (2008)CrossRefGoogle Scholar
  16. 16.
    Schmitt, N., Knuth, M., Bender, J., Kuijper, A.: Multilevel cloth simulation using GPU surface sampling. In: Proceedings of 10th Workshop on Virtual Reality Interactions and Physical Simulations, VRIPHYS 2013, Lille, France, pp. 1–10 (2013)Google Scholar
  17. 17.
    Bauer, F., Knuth, M., Kuijper, A., Bender, J.: Screen-space ambient occlusion using a-buffer techniques. In: 2013 International Conference on Computer-Aided Design and Computer Graphics, CAD/Graphics 2013, November 16–18, 2013, Guangzhou, China, pp. 140–147 (2013)Google Scholar
  18. 18.
    Kuijper, A.: P-laplacian driven image processing. In: Proceedings of the International Conference on Image Processing, ICIP 2007, September 16–19, 2007, San Antonio, Texas, USA, pp. 257–260 (2007)Google Scholar
  19. 19.
    Kuijper, A., Florack, L.: Understanding and modeling the evolution of critical points under Gaussian blurring. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 143–157. Springer, Heidelberg (2002). doi: 10.1007/3-540-47969-4_10 CrossRefGoogle Scholar
  20. 20.
    Kuijper, A.: Geometrical PDEs based on second-order derivatives of gauge coordinates in image processing. Image Vis. Comput. 27, 1023–1034 (2009)CrossRefGoogle Scholar
  21. 21.
    Julià, C., Sappa, A.D., Lumbreras, F., Serrat, J., López, A.: Recovery of surface normals and reflectance from different lighting conditions. In: Campilho, A., Kamel, M. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 315–325. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-69812-8_31 CrossRefGoogle Scholar
  22. 22.
    Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations. Applied Mathematical Sciences, vol. 147, 2nd edn. Springer, Heidelberg (2006)MATHGoogle Scholar
  23. 23.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Germann, M., Hornung, A., Keiser, R., Ziegler, R., Würmlin, S., Gross, M.: Articulated billboards for video-based rendering. Comput. Graph. Forum (Proc. Eurographics) 29, 585–594 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Technische Universität DarmstadtDarmstadtGermany
  2. 2.Fraunhofer IGDDarmstadtGermany

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