The Visual Computer

, Volume 31, Issue 6–8, pp 1001–1010 | Cite as

Visual importance-based adaptive photon tracing

  • Quan Zheng
  • Chang-Wen Zheng
Original Article


This paper proposes an adaptive photon tracing approach based on a novel importance function, which combines visual importance and photon path visibility. The generation of photon path is guided by sampling this function to trace more photons to visible and more contributive regions. As a first step, a hierarchy of visual importance maps is constructed. Next, photon paths are produced using a new hybrid mutation strategy, which consists of large mutation and small mutation. The mutation parameter used in small mutation is automatically adjusted using the adaptive Markov chain sampling method. Meanwhile, to find a suitable initial parameter, a mutation parameter initialization method is developed. Experiments show that, compared with previous methods, this approach yields results with better visual quality and smaller numerical error.


Visual importance Adaptive photon tracing Photorealistic rendering Global illumination 


  1. 1.
    Kajiya, J.T.: The rendering equation. In: Proceedings of the 13th Annual Conference on Computer Graphics and Interactive Techniques. SIGGRAPH’ 86, pp. 143–150. ACM, New York (1986)Google Scholar
  2. 2.
    Lafortune, E.P., Willems, Y.D.: Bi-directional path tracing. In: Proceedings of Third International Conference on Computational Graphics and Visualization Techniques (COMPUGRAPHICS’ 93), pp. 145–153 (1993)Google Scholar
  3. 3.
    Jensen, H.W.: Global illumination using photon maps. In: Proceedings of the Eurographics Workshop on Rendering Techniques’ 96, pp. 21–30. Springer, London (1996)Google Scholar
  4. 4.
    Hachisuka, T., Ogaki, S., Jensen, H.W.: Progressive photon mapping. ACM Trans. Graph. 27(5), 130:1–130:8 (2008)CrossRefGoogle Scholar
  5. 5.
    Hachisuka, T., Jensen, H.W.: Stochastic progressive photon mapping. ACM Trans. Graph. 28(5), 141:1–141:8 (2009)CrossRefGoogle Scholar
  6. 6.
    Fan, S., Chenney, S., Lai, Y.C.: Metropolis photon sampling with optional user guidance. In: Proceedings of the 16th Eurographics Symposium on Rendering, pp. 127–138. Eurographics Association, Aire-la-Ville (2005)Google Scholar
  7. 7.
    Chen, J., Wang, B., Yong, J.H.: Improved stochastic progressive photon mapping with metropolis sampling. Comput. Graph. Forum 30(4), 1205–1213 (2011)CrossRefGoogle Scholar
  8. 8.
    Hachisuka, T., Jensen, H.W.: Robust adaptive photon tracing using photon path visibility. ACM Trans. Graph. 30(5), 114:1–114:11 (2011)CrossRefGoogle Scholar
  9. 9.
    Veach, E., Guibas, L.J.: Metropolis light transport. Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques. SIGGRAPH’ 97, pp. 65–76. ACM, New York (1997)Google Scholar
  10. 10.
    Kelemen, C., Szirmay-Kalos, L., Antal, G., Csonka, F.: A simple and robust mutation strategy for the metropolis light transport algorithm. Comput. Graph. Forum 21(3), 531–540 (2002)CrossRefGoogle Scholar
  11. 11.
    Hoberock, J., Hart, J.C.: Arbitrary importance functions for metropolis light transport. Comput. Graph. Forum 29(6), 1993–2003 (2010)CrossRefGoogle Scholar
  12. 12.
    Kitaoka, S., Kitamura, Y., Kishino, F.: Replica exchange light transport. Comput. Graph. Forum 28(8), 2330–2342 (2009)CrossRefGoogle Scholar
  13. 13.
    Lehtinen, J., Karras, T., Laine, S., Aittala, M., Durand, F., Aila, T.: Gradient-domain metropolis light transport. ACM Trans. Graph. 32(4), 95:1–95:12 (2013)CrossRefGoogle Scholar
  14. 14.
    Collin, C., Ribardière, M., Gruson, A., Cozot, R., Pattanaik, S., Bouatouch, K.: Visibility-driven progressive volume photon tracing. Vis. Comput. 29(9), 849–859 (2013)CrossRefGoogle Scholar
  15. 15.
    Craiu, R.V., Rosenthal, J., Yang, C.: Learn from thy neighbor: parallel-chain and regional adaptive mcmc. J. Am. Stat. Assoc. 104(488), 1454–1466 (2009)zbMATHMathSciNetCrossRefGoogle Scholar
  16. 16.
    Christensen, P.H.: Adjoints and importance in rendering: an overview. IEEE Trans. Vis. Comput. Graph. 9(3), 329–340 (2003)CrossRefGoogle Scholar
  17. 17.
    Peter, I., Pietrek, G.: Importance driven construction of photon maps. In: Proceedings of the 9th Eurographics Workshop on Rendering Techniques’ 98, pp. 269–280. Springer, London (1998)Google Scholar
  18. 18.
    Bashford-Rogers, T., Debattista, K., Chalmers, A.: Importance driven environment map sampling. IEEE Trans. Vis. Comput. Graph. 20(6), 907–918 (2014)CrossRefGoogle Scholar
  19. 19.
    Vorba, J., Karlík, O., Šik, M., Ritschel, T., Křivánek, J.: On-line learning of parametric mixture models for light transport simulation. ACM Trans. Graph. 33(4), 101 (2014)CrossRefGoogle Scholar
  20. 20.
    Atchadé, Y.F., Rosenthal, J.S.: On adaptive markov chain Monte Carlo algorithms. Bernoulli 11(5), 815–828 (2005)zbMATHMathSciNetCrossRefGoogle Scholar
  21. 21.
    Roberts, G.O., Rosenthal, J.S.: Optimal scaling for various metropolis-hastings algorithms. Stat. Sci. 16(4), 351–367 (2001)zbMATHMathSciNetCrossRefGoogle Scholar
  22. 22.
    Andrieu, C., Robert, C.P.: Controlled mcmc for optimal sampling. Tech. rep, Centre de Recherche en Economie et Statistique (2001)Google Scholar
  23. 23.
    Kesten, H.: Accelerated stochastic approximation. The Annals of Mathematical Statistics, pp. 41–59 (1958)Google Scholar
  24. 24.
  25. 25.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to struc-tural similarity. IEEE Trans. Image. Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Science and Technology on Integrated Information System LaboratoryInstitute of Software, Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations