“Robust Adaptive Photon Tracing Using Photon-Path Visibility” by Hachisuka and Jensen

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    Robust Adaptive Photon Tracing Using Photon-Path Visibility

Session/Category Title:   Global Illumination


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Abstract:


    We present a new adaptive photon tracing algorithm which can handle illumination settings that are considered difficult for photon tracing approaches such as outdoor scenes, close-ups of a small part of an illuminated region, and illumination coming through a small gap. The key contribution in our algorithm is the use of visibility of photon path as the importance function which ensures that our sampling algorithm focuses on paths that are visible from the given viewpoint. Our sampling algorithm builds on two recent developments in Markov chain Monte Carlo methods: adaptive Markov chain sampling and replica exchange. Using these techniques, each photon path is adaptively mutated and it explores the sampling space efficiently without being stuck at a local peak of the importance function. We have implemented this sampling approach in the progressive photon mapping algorithm which provides visibility information in a natural way when a photon path contributes to a measurement point. We demonstrate that the final algorithm is strikingly simple, yet effective at sampling photons under lighting conditions that would be difficult for existing Monte Carlo ray tracing-based methods.

References:


    1. Andrieu, C. and Robert, C. P. 2001. Controlled mcmc for optimal sampling. Tech. rep. 0125, Cahiers de Mathématiques du Ceremade, UniversitéParis-Dauphine.
    2. Andrieu, C. and Thoms, J. 2008. A tutorial on adaptive MCMC. Statist. Comput. 18, 4, 343–373.
    3. Arvo, J. 1986. Backward ray tracing. In ACM SIGGRAPH Course Notes, Developments in Ray Tracing, 259–263.
    4. Cline, D., Talbot, J., and Egbert, P. 2005. Energy redistribution path tracing. ACM Trans. Graph. 24, 3, 1186–1195.
    5. Dutré, P., Lafortune, E., and Willems, Y. 1993. Monte carlo light tracing with direct computation of pixel intensities. In Proceedings of Compugraphics Conference. 128–137.
    6. Fan, S., Chenney, S., and chi Lai, Y. 2005. Metropolis photon sampling with optional user guidance. In Proceedings of the 16th Eurographics Symposium on Rendering Techniques. Eurographics Association, 127–138.
    7. Haario, H., Saksman, E., and Tamminen, J. 2001. An adaptive metropolis algorithm. Bernoulli 7, 2, 223–242.
    8. Hachisuka, T. and Jensen, H. W. 2009. Stochastic progressive photon mapping. In ACM SIGGRAPH Asia Papers. ACM, New York, 1–8.
    9. Hachisuka, T., Ogaki, S., and Jensen, H. W. 2008. Progressive photon mapping. ACM Trans. Graph. 27, 5, Article 130.
    10. Hoberock, J. and Hart, J. C. 2010. Arbitrary importance functions for metropolis light transport. Comput. Graph. Forum 29, 6, 1993–2003.
    11. Iba, Y. 2001. Extended ensemble monte carlo. Int. J. Modern Phys. C 12, 05, 623–656.
    12. Jensen, H. W. 1996. Global illumination using photon maps. In Proceedings of the Eurographics Workshop on Rendering Techniques. Springer, 21–30.
    13. Kajiya, J. T. 1986. The rendering equation. ACM Comput. Graph. 20, 4, 143–150.
    14. Kelemen, C., Szirmay-Kalos, L., Antal, G., and Csonka, F. 2002. A simple and robust mutation strategy for the metropolis light transport algorithm. Comput. Graph. Forum. 531–540.
    15. Kitaoka, S., Kitamura, Y., and Kishino, F. 2009. Replica exchange light transport. Comput. Graph. Forum 28, 8, 2330–2342.
    16. Lafortune, E. P. and Willems, Y. D. 1993. Bi-Directional path tracing. In Proceedings of Compugraphics Conference. H. P. Santo, Ed., 145–153.
    17. Roberts, G. O., Gelman, A., and Gilks, W. R. 1997. Weak convergence and optimal scaling of random walk metropolis algorithms. Ann. Appl. Probab. 7, 1, 110–120.
    18. Rosenthal, J. S., Brooks, S., Gelman, A., Jones, G., and l. Meng, X. 2008. Optimal proposal distributions and adaptive MCMC. In MCMC Handbook.
    19. Segovia, B., Iehl, J.-C., and Peroche, B. 2007. Coherent metropolis light transport with multiple-try mutations. Tech. rep. RR-LIRIS-2007-015.
    20. Swendsen, R. H. and Wang, J.-S. 1986. Replica monte carlo simulation of spin-glasses. Phys. Rev. Lett. 57, 21, 2607+.
    21. Veach, E. 1998. Robust monte carlo methods for light transport simulation. Ph.D. thesis, Stanford, CA. J. Guibas.
    22. Veach, E. and Guibas, L. J. 1995. Optimally combining sampling techniques for monte carlo rendering. ACM Comput. Graph. 419–428.
    23. Veach, E. and Guibas, L. J. 1997. Metropolis light transport. ACM Comput. Graph. 65–76.

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