Advertisement

Extracting-mapping scheme for the dynamic details in fluid re-simulations from videos

  • Hongyan QuanEmail author
  • Ning Wang
  • Jimeng Li
  • Changbo Wang
Regular Paper
  • 22 Downloads

Abstract

Reconstructing a 3D fluid simulation (re-simulation) from video has practical significance. In this study, we address the realistic problem of fluid re-simulation in an inverse project. State-of-the-art studies on the inverse problem of fluid simulation have mainly focused on dimensionality reduction for acquiring better time performance, but the realistic aspect has rarely been investigated. This paper presents an extracting-mapping scheme to tightly couple fluid re-simulation with enhanced physically driven data for realistic high-quality dynamic detail. We make a full use of the details extracted from recovered physically driven data to improve the coarse and unrealistic re-simulation from fluid auto-advection. Two schemes are discussed. The first scheme is the density block method (DBM). In this method, a density block database is constructed from the prepartitioned and sorted density blocks, and then, some selected density blocks with dynamic details are selected from the preconstructed database and coupled coherently into the physically driven data to enhance the detail in every auto-advection cycle. The second method is the density spectrum block method (DSBM) in the frequency domain. Using the DSBM and DBM, realistic effects are achieved by extensive quantitative and qualitative evaluation via re-simulation tests driven by the recovered physical data from ground truth fluid video. Both approaches outperform previous auto-advection re-simulation schemes in terms of the rich detail under several challenging scenarios and low-level hardware conditions.

Keywords

Fluid Re-simulation Dynamic detail Density 

Notes

Acknowledgements

We thank Dyntex for providing rich fluid videos for our study. We also thank Xinquan Zhou for her help in the preliminary work. Special thanks to the reviewers for their valuable comments and suggestions.

Funding

This study was funded by the NSFC Grant No. 61672237, 61532002, and the National High-tech R&D Program of China (863 Program) under Grant 2015AA016404.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest concerning this paper.

References

  1. 1.
    Solenthaler, B., Pajarola, R.: Predictive-corrective incompressible SPH. ACM Trans. Gr. 28(3), 1–6 (2009)CrossRefGoogle Scholar
  2. 2.
    Charypar, D., Gross, M.: Particle-based fluid simulation for interactive applications. In: ACM Siggraph/Eurographics Symposium on Computer Animation. Eurographics association, pp 154–159 (2003)Google Scholar
  3. 3.
    Liu, M.B., Liu, G.R.: Smoothed particle hydrodynamics (SPH): an overview and recent developments. Arch. Comput. Methods Eng. 17(1), 25–76 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Kuznik, F., Obrecht, C., et al.: LBM based flow simulation using GPU computing processor. Comput. Math. Appl. 59(7), 2380–2392 (2010)CrossRefzbMATHGoogle Scholar
  5. 5.
    Zhu, Y., Bridson, R.: Animating sand as a fluid. ACM Trans. Gr. 24(3), 965–972 (2005)CrossRefGoogle Scholar
  6. 6.
    Enright, D., Fedkiw, R., Ferziger, J., et al.: A hybrid particle level set method for improved interface capturing. J. Comput. Phys. 183(1), 83–116 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Yan, Z., Zhu, X., Liu, Y., et al.: An improved method of position based fluids. Coll. Math. 32(1), 38–43 (2016)MathSciNetGoogle Scholar
  8. 8.
    Boyd, L., Bridson, R.: MultiFLIP for energetic two-phase fluid simulation. ACM Trans. Gr. 31(2), 1–12 (2012)CrossRefGoogle Scholar
  9. 9.
    Huang, T., Li, D., Li, L.: Adaptive time-stepping particle fluid motion simulation. In: International Conference on Computer Application and System Modeling. IEEE, V12-117-V12-121 (2010)Google Scholar
  10. 10.
    Jeong, S.H., Solenthaler, B., Pollefeys, M., et al.: Data-driven fluid simulations using regression forests. ACM Trans. Gr. 34(6), 199 (2015)Google Scholar
  11. 11.
    Yang, C., Yang, X., Xiao, X.: Data-driven projection method in fluid simulation. Comput. Anim. Vir. Worlds 27(3–4), 415–424 (2016)CrossRefGoogle Scholar
  12. 12.
    Okabe, M., Anjyor, K., Onai, R.: Creating fluid animation from a single image using video database. Comput. Gr. Forum 30(7), 1973–1982 (2011)CrossRefGoogle Scholar
  13. 13.
    Sato, S..,Morita, T., Dobashi, Y., Yamamoto, T.: A data-driven approach for synthesizing high-resolution animation of fire. In: Proceeding of the Digital Production Symposium, pp 37–42 (2012)Google Scholar
  14. 14.
    Quan, H., Wang, C., Song, Y.: Fluid re-simulation based on physically driven model from video. Vis. Comput. 33(1), 85–98 (2017)CrossRefGoogle Scholar
  15. 15.
    Mullen, P., Crane, K., Pavlov, D.: Energy-preserving integrators for fluid animation. ACM Trans. Gr. 28(3), 38,1–8 (2009)CrossRefGoogle Scholar
  16. 16.
    Lentine, M., Zheng, W., Fedkiw, R.: A novel algorithm for incompressible flow using only a coarse grid projection. ACM Trans. Gr. 29(4), 114 (2010)CrossRefGoogle Scholar
  17. 17.
    Péteri, R., Fazekas, S., Huiskes, M.J.: DynTex: a comprehensive database of dynamic textures. Pattern Recogn. Lett. 31(12), 1627–1632 (2010)CrossRefGoogle Scholar
  18. 18.
    Chenggang, Y., Hongtao, X., Dongbao, Y., Jian, Y., Yongdong, Z., Qionghai, D.: Supervised hash coding with deep neural network for environment perception of intelligent vehicles. In: IEEE Transactions on Intelligent Transportation Systems, 99,10,1-12 (2017)Google Scholar
  19. 19.
    Chenggang, Y., Hongtao, X., Shun, L., Jian, Y., Yongdong, Z., Qionghai, D.: Effective uyghur language text detection in complex background images for traffic prompt identification. IEEE Trans. Intell. Transp. Syst. 99(10), 1–10, (2017)Google Scholar
  20. 20.
    Chenggang, Y., Zhang, Y., Xu, J., et al: A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process. Lett. 21(5), 573–576, (2014)CrossRefGoogle Scholar
  21. 21.
    Yan, C., Zhang, Y., Xu, J., et al: Efficient parallel framework for HEVC motion estimation on many-core processors[J]. IEEE Trans. Circ. Syst. Video Technol. 24(12), 2077–2089 (2014)CrossRefGoogle Scholar
  22. 22.
    Miguel, E., Bradley, D., Thomaszewski, B., et al.: Data-driven estimation of cloth simulation models. Comput. Gr. Forum 31(2), 519–528 (2012)CrossRefGoogle Scholar
  23. 23.
    Otaduy, M.A., Bickel, et al.: Data-driven simulation methods in computer graphics: cloth, tissue and faces. In: Eurographics (2013)Google Scholar
  24. 24.
    White, R., Crane, K., Forsyth, D.A.: Data driven cloth animation. In: ACM Siggraph 2007 sketches, p 37 (2007)Google Scholar
  25. 25.
    Wang, C., Wang, C., Qin, H., et al.: Video-based fluid reconstruction and its coupling with SPH simulation. Vis. Comput. 33, 1–14(2016)Google Scholar
  26. 26.
    Kim, T., Delaney, J.: Subspace fluid re-simulation. ACM Trans. Gr. 32(4), 1–9 (2013)zbMATHGoogle Scholar
  27. 27.
    Kenichi, S.: Interactive SPH simulation of fluid phenomena. J. Vis. Soc. Jpn. 21(2), 15–18 (2001)Google Scholar
  28. 28.
    Kim, T., Thürey, N., James, D.: Wavelet turbulence for fluid simulation. ACM Trans. Gr. 27(3), 1–6 (2008)CrossRefGoogle Scholar
  29. 29.
    Klingner, B.M., Feldman, B.E., Chentanez, N.: fluid animation with dynamic meshes. ACM Trans. Gr. 25(3), 820–825 (2006)CrossRefGoogle Scholar
  30. 30.
    Kuznik, F., Obrecht, C., Rusaouen, G., Roux, J.J.: Lbm based flow simulation using gpu computing processor. Comput. Math. Appl. 59(7), 2380–2392 (2010)CrossRefzbMATHGoogle Scholar
  31. 31.
    https://www.opengl.org/. Accessed 22 Dec 2017
  32. 32.
    Yi, W., Jiangyun, W., Xiao, S., Liang, H.: An efficient adaptive fuzzy switching weighted mean filter for salt-and-pepper noise removal. IEEE Signal Process. Lett. 23(11), 1582–1586 (2016)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.The School of Computer Science and Software EngineeringEast China Normal UniversityShanghaiChina

Personalised recommendations