3D Research

, 9:1 | Cite as

Screen Space Ambient Occlusion Based Multiple Importance Sampling for Real-Time Rendering

  • Abd El Mouméne Zerari
  • Mohamed Chaouki Babahenini
3DR Express

Abstract

We propose a new approximation technique for accelerating the Global Illumination algorithm for real-time rendering. The proposed approach is based on the Screen-Space Ambient Occlusion (SSAO) method, which approximates the global illumination for large, fully dynamic scenes at interactive frame rates. Current algorithms that are based on the SSAO method suffer from difficulties due to the large number of samples that are required. In this paper, we propose an improvement to the SSAO technique by integrating it with a Multiple Importance Sampling technique that combines a stratified sampling method with an importance sampling method, with the objective of reducing the number of samples. Experimental evaluation demonstrates that our technique can produce high-quality images in real time and is significantly faster than traditional techniques.

Keywords

Soft shadows GPU Global illumination Real-time rendering SSAO Multiple importance sampling 

References

  1. 1.
    Mittring, M. (2007). Finding next gen: Cryengine 2. In Proceeding of SIGGRAPH 2007, advanced real-time rendering in 3D graphics and games course, Chapter 8, ACM, New York, NY, USA, (pp. 97–121).  https://doi.org/10.1145/1281500.1281671.
  2. 2.
    Mittring, M. (2012). The technology behind the unreal engine 4 elemental demo. In Proceeding of SIGGRAPH 2012: 39th international conference and exhibition on computer graphics and interactive techniques, Advances in Real-Time Rendering in 3D Graphics and Games course, ACM, Los Angeles Convention Center.Google Scholar
  3. 3.
    Sunet, M., & Vazquez, P. (2016). Optimized screen-space ambient occlusion in mobile devices. In The proceedings of the 21st international conference on Web3D technology (pp. 127–135), Anaheim, California. ISBN:978-1-4503-4428-9.  https://doi.org/10.1145/2945292.2945300.
  4. 4.
    Filion, D., & McNaughton, R. (2008). Effects and techniques. In Proceeding ACM SIGGRAPH 2008 games (pp. 133–164).  https://doi.org/10.1145/1404435.1404441.
  5. 5.
    Jimenez, J., Wu, X., Pesce, A., & Jarabo, A. (2016). Practical real-time strategies for accurate indirect occlusion. In Proceeding of SIGGRAPH 2016 Courses: Physically based shading in theory and practice (pp. 112–161).Google Scholar
  6. 6.
    McGuire, M., Osman, B., Bukowski, M., & Hennessy, P. (2011). The alchemy screen-space ambient obscurance algorithm. In Proceeding: HPG ’11 proceedings of the ACM SIGGRAPH symposium on high performance graphics (pp. 25–32). Vancouver, British Columbia, Canada August 05–07, 2011. ISBN:978-1-4503-0896-0.  https://doi.org/10.1145/2018323.2018327.
  7. 7.
    Bavoil, L., Sainz, M., & Dimitrov, R. (2008). Image-space horizon-based ambient occlusion. In Proceeding: ACM SIGGRAPH 2008 talks (pp. 22:1–22:1) Los Angeles, California, . ISBN:978-1-60558-343-3.  https://doi.org/10.1145/1401032.1401061.
  8. 8.
    Rubinstein, R. Y. (1981). Simulation and the Monte Carlo method. New York, NY, USA: Wiley.CrossRefMATHGoogle Scholar
  9. 9.
    Owen, A., & Zhou, Y. (2000). Safe and effective importance sampling. Journal of the American Statistical Association, 95(449), 135–143.  https://doi.org/10.2307/2669533. (Taylor & Francis, Ltd. on behalf of the American Statistical Association).MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Veach, E. (1997). Robust Monte Carlo methods for light transport simulation. Ph.D. thesis. Stanford University. ISBN:0-591-90780-1.Google Scholar
  11. 11.
    Luft, T., Coloditz, C. & Deussen, O. (2006). Image enhancement by unsharp masking the depth buffer. In Journal of ACM Transactions on Graphics (TOG)—Proceedings of ACM SIGGRAPH 2006 (Vol. 25(3), pp. 1206–1213). ISBN:1-59593-364-6.  https://doi.org/10.1145/1141911.1142016.
  12. 12.
    Zhukov, S., Iones, A., & Kronin, G. (1998). An ambient light illumination model. In G. Drettakis & N. Max (Eds.), Proceedings of eurographics rendering workshop 98 (pp. 45–56). Springer: Berlin, Heidelberg, New York.Google Scholar
  13. 13.
    Landis, H. (2002). Production-ready global illumination. RenderMan in production. Course 16: ACM SIGGRAPH 2002 course notes. Boston: ACM.Google Scholar
  14. 14.
    Bunnell, M. (2005). Dynamic ambient occlusion and indirect lighting. In GPU Gems: Programming techniques for high-performance graphics and general-purpose computation, Chapter 14 (Vol. 2(2), pp. 223–233), Addison-Wesley Professional. ISBN 0-321-33559-7.Google Scholar
  15. 15.
    McGuire, M. (2010). Ambient occlusion volumes. In Proceedings of the conference on high performance graphics (pp. 47–56). Saarbrucken, Germany.Google Scholar
  16. 16.
    Hernell, F., Ljung, P., & Ynnerman, A. (2010). Local ambient occlusion in direct volume rendering. IEEE Transactions on Visualization and Computer Graphics, 16(4), 548–559.  https://doi.org/10.1109/TVCG.2009.45.CrossRefGoogle Scholar
  17. 17.
    Grottel, S., Krone, M., Scharnowski, K. & Ertl, T. (2012). Object-space ambient occlusion for molecular dynamics. In Pacific visualization symposium (PacificVis), 2012 IEEE (pp. 209–216).  https://doi.org/10.1109/PacificVis.2012.6183593.
  18. 18.
    Amanatides, J., Woo, A., et al. (1987). A fast voxel traversal algorithm for ray tracing. Eurographics, 87(3), 3–10. (ISSN:1017-4656).Google Scholar
  19. 19.
    Vardis, K., & Gaitatzes, G. (2013). Multi-view ambient occlusion with importance sampling. In Proceeding I3D ’13 proceedings of the ACM SIGGRAPH symposium on interactive 3D graphics and games (pp. 111–118). Orlando, Florida March 21–23, 2013. ACM New York, NY, USA 2013. ISBN:978-1-4503-1956-0.  https://doi.org/10.1145/2448196.2448214.
  20. 20.
    Williams, L. (1978). Casting curved shadows on curved surfaces. In ACM SIGGRAPH ’78 proceedings of the 5th annual conference on computer graphics and interactive techniques (Vol. 12(3), pp. 270–274).Google Scholar
  21. 21.
    Cox, P., Yang, P., Mahant-Shetti, S. S., & Chatterjee, P. (1985). Statistical modeling for efficient parametric yield estimation of MOS VLSI circuits. IEEE Transactions Electron Devices, 20(1), 471–478.  https://doi.org/10.1109/JSSC.1985.1052319.CrossRefGoogle Scholar
  22. 22.
    Dutré, P. (2003). Global illumination compendium. In The Journal of Computer Graphics, Cornell University: Department of Computer Science, Katholieke Universiteit Leuven.Google Scholar
  23. 23.
    Phong, B. T. (1975). Illumination for computer generated images. Communications of the ACM, 18(6), 311–317.CrossRefGoogle Scholar
  24. 24.
    Paris, S., Kornprobst, P., Tumblin, J., & Durand, F. (2009). Bilateral filtering: Theory and applications. Journal of Foundations and Trends, in Computer Graphics and Vision, 4(1), 1–73.  https://doi.org/10.1561/0600000020.MATHGoogle Scholar
  25. 25.
    Reinhard, E., Stark M., Shirley P., & Ferwerda J. (2002). Photographic tone reproduction for digital images. In Proceedings of SIGGRAPH 2002, ACM transactions on graphics (TOG homepage) (Vol. 21(3), pp. 267–276).  https://doi.org/10.1145/566570.566575.
  26. 26.
    Willmott, C., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Published in Climate Research, 30, 7982.  https://doi.org/10.3354/cr030079.Google Scholar
  27. 27.
    Mantiuk, R., Kim, K. J., Rempel, A. G., & Heidrich, W. (2011). Hdr-vdp-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions. In Journal ACM transactions on graphics (TOG)—Proceedings of ACM SIGGRAPH 2011 (TOG Homepage), (Vol. 30(4), p. 40). ISBN:978-1-4503-0943-1.  https://doi.org/10.1145/1964921.1964935.
  28. 28.
    McGuire, M. (2017). Computer graphics archive, July 2017. http://casual-effects.com/data/index.html.
  29. 29.
    Stanford University: The stanford 3D scanning repository, 1996. http://graphics.stanford.edu/data/3Dscanrep/.

Copyright information

© 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceLESIA, University of Mohamed KhiderBiskraAlgeria

Personalised recommendations