Advertisement

The Visual Computer

, Volume 35, Issue 12, pp 1827–1840 | Cite as

An asynchronous method for cloud-based rendering

  • Keith BugejaEmail author
  • Kurt Debattista
  • Sandro Spina
Original Article
  • 130 Downloads

Abstract

Interactive high-fidelity rendering is still unachievable on many consumer devices. Cloud gaming services have shown promise in delivering interactive graphics beyond the individual capabilities of user devices. However, a number of shortcomings are manifest in these systems: high network bandwidths are required for higher resolutions and input lag due to network fluctuations heavily disrupts user experience. In this paper, we present a scalable solution for interactive high-fidelity graphics based on a distributed rendering pipeline where direct lighting is computed on the client device and indirect lighting in the cloud. The client device keeps a local cache for indirect lighting which is asynchronously updated using an object space representation; this allows us to achieve interactive rates that are unconstrained by network performance for a wide range of display resolutions that are also robust to input lag. Furthermore, in multi-user environments, the computation of indirect lighting is amortised over participating clients.

Keywords

Rendering Rasterisation Global illumination Distributed algorithms Cloud computing 

Supplementary material

Supplementary material 1 (mp4 112983 KB)

Supplementary material 2 (mp4 50294 KB)

Supplementary material 3 (mp4 68100 KB)

Supplementary material 4 (mp4 72763 KB)

Supplementary material 5 (mp4 57348 KB)

References

  1. 1.
    Autodesk 360 (2014). http://www.autodesk.com/products/rendering/overview. Accessed 31 July 2017
  2. 2.
    Ahmed, A.G., Niese, T., Huang, H., Deussen, O.: An adaptive point sampler on a regular lattice. ACM Trans. Graph. 36(4), 138 (2017)CrossRefGoogle Scholar
  3. 3.
    Bashford-Rogers, T., Debattista, K., Chalmers, A.: Importance driven environment map sampling. IEEE Trans. Vis. Comput. Graph. 20, 907–918 (2013)CrossRefGoogle Scholar
  4. 4.
    Bierton, D.: Face-off: Gaikai vs. onlive (2012). http://www.eurogamer.net/articles/digitalfoundry-face-off-gaikai-vs-onlive. Accessed 11 July 2018
  5. 5.
    Bikker, J., Reijerse, R.: A precalculated point set for caching shading information. In: Eurographics 2009-Short Papers, pp. 65–68. The Eurographics Association (2009)Google Scholar
  6. 6.
    Brouillat, J., Gautron, P., Bouatouch, K.: Photon-Driven Irradiance Cache. Computer Graphics Forum, Wiley Online Library, vol. 27, pp. 1971–1978. Wiley, Oxford (2008)Google Scholar
  7. 7.
    Bugeja, K., Debattista, K., Spina, S., Chalmers, A.: Collaborative high-fidelity rendering over peer-to-peer networks. In: Eurographics Symposium on Parallel Graphics and Visualization, pp. 9–16. The Eurographics Association (2014)Google Scholar
  8. 8.
    Chalmers, A., Reinhard, E., Davis, T.: Practical Parallel Rendering. CRC Press, Boca Raton (2002)Google Scholar
  9. 9.
    Crassin, C., Luebke, D., Mara, M., McGuire, M., Oster, B., Shirley, P., Sloan, P.P., Wyman, C.: CloudLight: a system for amortizing indirect lighting in real-time rendering. J. Comput. Graph. Tech. 4(4), 1–27 (2015)Google Scholar
  10. 10.
    Crassin, C., Neyret, F., Sainz, M., Green, S., Eisemann, E.: Interactive Indirect Illumination Using Voxel Cone Tracing. Computer Graphics Forum, Wiley Online Library, vol. 30, pp. 1921–1930. Wiley, Oxford (2011)Google Scholar
  11. 11.
    Dachsbacher, C., Křivánek, J., Hašan, M., Arbree, A., Walter, B., Novák, J.: Scalable Realistic Rendering with Many-Light Methods. Computer Graphics Forum, Wiley Online Library, vol. 33, pp. 88–104. Wiley, Oxford (2014)Google Scholar
  12. 12.
    Dachsbacher, C., Stamminger, M.: Reflective shadow maps. In: Proceedings of the 2005 Symposium on Interactive 3D graphics and games, pp. 203–231. ACM (2005)Google Scholar
  13. 13.
    Dammertz, H., Keller, A., Lensch, H.P.: Progressive Point-Light-Based Global Illumination. Computer Graphics Forum, Wiley Online Library. Wiley, Oxford (2010)Google Scholar
  14. 14.
    Debattista, K., Dubla, P., Banterle, F., Santos, L.P., Chalmers, A.: Instant Caching for Interactive Global Illumination. Computer Graphics Forum, Wiley Online Library, vol. 28, pp. 2216–2228. Wiley, Oxford (2009)Google Scholar
  15. 15.
    Deering, M., Winner, S., Schediwy, B., Duffy, C., Hunt, N.: The triangle processor and normal vector shader: a VLSI system for high performance graphics. In: ACM SIGGRAPH Computer Graphics, vol. 22, pp. 21–30. ACM (1988)Google Scholar
  16. 16.
    Dippé, M.A., Wold, E.H.: Antialiasing through stochastic sampling. In: ACM Siggraph Computer Graphics, vol. 19(3), pp. 69–78 (1985)CrossRefGoogle Scholar
  17. 17.
    Gamito, M.N., Maddock, S.C.: Accurate multidimensional poisson-disk sampling. ACM Trans. Graph. TOG 29(1), 8 (2009)Google Scholar
  18. 18.
    Jensen, H.W.: Realistic Image Synthesis Using Photon Mapping. AK Peters, Ltd., Natick (2001)zbMATHCrossRefGoogle Scholar
  19. 19.
    Kajiya, J.T.: The rendering equation. In: ACM Siggraph Computer Graphics, vol. 20, pp. 143–150 (1986)CrossRefGoogle Scholar
  20. 20.
    Kaplanyan, A., Dachsbacher, C.: Cascaded light propagation volumes for real-time indirect illumination. In: Proceedings of the 2010 ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, pp. 99–107. ACM (2010)Google Scholar
  21. 21.
    Keller, A.: Instant radiosity. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, pp. 49–56. ACM Press/Addison-Wesley Publishing Co. (1997)Google Scholar
  22. 22.
    Kopf, J., Cohen, M.F., Lischinski, D., Uyttendaele, M.: Joint bilateral upsampling. ACM Trans. Graph. 26, 96 (2007)CrossRefGoogle Scholar
  23. 23.
    Langer, M.S., Bülthoff, H.H.: Depth discrimination from shading under diffuse lighting. Perception 29(6), 649–660 (1999)CrossRefGoogle Scholar
  24. 24.
    Lehmann, M.A.: LZF compression library (LibLZF) (2014). http://oldhome.schmorp.de/marc/liblzf.html. Accessed 11 July 2018
  25. 25.
    Lewis, M.: The new cards. Commun. ACM 45(1), 30–31 (2002)Google Scholar
  26. 26.
    Liu, C., Jia, J., Zhang, Q., Zhao, L.: Lightweight websim rendering framework based on cloud-baking. In: Proceedings of the 2017 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, pp. 221–229. ACM (2017)Google Scholar
  27. 27.
    Manzano, M., Hernández, J.A., Uruenña, M., Calle, E.: An empirical study of cloud gaming. In: Proceedings of the 11th Annual Workshop on Network and Systems Support for Games, p. 17. IEEE Press (2012)Google Scholar
  28. 28.
    Mara, M., Luebke, D., McGuire, M.: Toward practical real-time photon mapping: efficient GPU density estimation. In: Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, pp. 71–78. ACM (2013)Google Scholar
  29. 29.
    McGuire, M., Luebke, D.: Hardware-accelerated global illumination by image space photon mapping. In: Proceedings of the 2009 ACM SIGGRAPH/EuroGraphics Conference on High Performance Graphics. ACM, New York, NY, USA (2009)Google Scholar
  30. 30.
    Mitchell, J., McTaggart, G., Green, C.: Shading in valve’s source engine. In: ACM SIGGRAPH 2006 Courses, pp. 129–142. ACM (2006)Google Scholar
  31. 31.
    Mittring, M.: Finding next gen: Cryengine 2. In: ACM SIGGRAPH 2007 Courses, SIGGRAPH ’07, pp. 97–121. ACM, New York, NY, USA (2007)Google Scholar
  32. 32.
    Myszkowski, K., Tawara, T., Akamine, H., Seidel, H.P.: Perception-guided global illumination solution for animation rendering. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 221–230. ACM (2001)Google Scholar
  33. 33.
    Pajak, D., Herzog, R., Eisemann, E., Myszkowski, K., Seidel, H.P.: Scalable Remote Rendering with Depth and Motion-Flow Augmented Streaming. Computer Graphics Forum, Wiley Online Library. Wiley, Oxford (2011)Google Scholar
  34. 34.
    Pál, L., Oláh-Gál, R., Makó, Z.: Shepard interpolation with stationary points. Acta Univ. Sapientiae 1(1), 5–13 (2009)MathSciNetzbMATHGoogle Scholar
  35. 35.
    Pilleboue, A., Singh, G., Coeurjolly, D., Kazhdan, M., Ostromoukhov, V.: Variance analysis for monte carlo integration. ACM Trans. Graph. 34(4), 124 (2015)zbMATHCrossRefGoogle Scholar
  36. 36.
    Qin, H., Chen, Y., He, J., Chen, B.: Wasserstein blue noise sampling. ACM Trans. Graph. 36(5), 168 (2017)CrossRefGoogle Scholar
  37. 37.
    Render Rocket (2014). http://www.renderrocket.com. Accessed 11 July 2018
  38. 38.
    Saito, T., Takahashi, T.: Comprehensible rendering of 3-d shapes. In: ACM SIGGRAPH Computer Graphics, vol. 24, pp. 197–206. ACM (1990)Google Scholar
  39. 39.
    Shepard, D.: A two-dimensional interpolation function for irregularly-spaced data. In: Proceedings of the 1968 23rd ACM National Conference, pp. 517–524. ACM (1968)Google Scholar
  40. 40.
    Wachtel, F., Pilleboue, A., Coeurjolly, D., Breeden, K., Singh, G., Cathelin, G., De Goes, F., Desbrun, M., Ostromoukhov, V.: Fast tile-based adaptive sampling with user-specified fourier spectra. ACM Trans. Graph. 33(4), 56 (2014)CrossRefGoogle Scholar
  41. 41.
    Walter, B., Drettakis, G., Parker, S.: Interactive rendering using the render cache. In: Lischinski, D., Larson, G.W. (eds.) Rendering Techniques 99, pp. 19–30. Springer, Berlin (1999)CrossRefGoogle Scholar
  42. 42.
    Ward, G.J., Rubinstein, F.M., Clear, R.D.: A ray tracing solution for diffuse interreflection. ACM SIGGRAPH Comput. Graph. 22(4), 85–92 (1988)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of MaltaMsidaMalta
  2. 2.WMGUniversity of WarwickCoventryUK

Personalised recommendations