“Gradient-domain metropolis light transport” by Lehtinen, Karras, Laine, Aittala, Durand, et al. …
Conference:
Type(s):
Title:
- Gradient-domain metropolis light transport
Session/Category Title: Global Illumination
Presenter(s)/Author(s):
Moderator(s):
Abstract:
We introduce a novel Metropolis rendering algorithm that directly computes image gradients, and reconstructs the final image from the gradients by solving a Poisson equation. The reconstruction is aided by a low-fidelity approximation of the image computed during gradient sampling. As an extension of path-space Metropolis light transport, our algorithm is well suited for difficult transport scenarios. We demonstrate that our method outperforms the state-of-the-art in several well-known test scenes. Additionally, we analyze the spectral properties of gradient-domain sampling, and compare it to the traditional image-domain sampling.
References:
1. Bhat, P., Zitnick, L., Cohen, M., and Curless, B. 2010. GradientShop: A gradient-domain optimization framework for image and video filtering. ACM Trans. Graph. 29, 2, 10:1–10:14. Google ScholarDigital Library
2. Bolin, M. R., and Meyer, G. W. 1995. A frequency based ray tracer. In Proc. ACM SIGGRAPH 95, 409–418. Google ScholarDigital Library
3. Cline, D., Talbot, J., and Egbert, P. 2005. Energy redistribution path tracing. ACM Trans. Graph. 24, 3, 1186–1195. Google ScholarDigital Library
4. Dayal, A., Woolley, C., Watson, B., and Luebke, D. 2005. Adaptive frameless rendering. In Proc. Eurographics Symposium on Rendering 2005. Google ScholarDigital Library
5. Egan, K., Tseng, Y., Holzschuch, N., Durand, F., and Ramamoorthi, R. 2009. Frequency analysis and sheared reconstruction for rendering motion blur. ACM Trans. Graph. 28, 3, 93:1–93:13. Google ScholarDigital Library
6. Georgiev, T. 2005. Image reconstruction invariant to relighting. In Proc. Eurographics 2005, 61–64.Google Scholar
7. Hachisuka, T., Jarosz, W., Weistroffer, R. P., Dale, K., Humphreys, G., Zwicker, M., and Jensen, H. W. 2008. Multidimensional adaptive sampling and reconstruction for ray tracing. ACM Trans. Graph. 27, 3, 33:1–33:10. Google ScholarDigital Library
8. Hastings, W. 1970. Monte Carlo samping methods using Markov chains and their applications. Biometrika 57, 1, 97–109.Google ScholarCross Ref
9. Hoberock, J., and Hart, J. C. 2010. Arbitrary importance functions for Metropolis light transport. Comput. Graph. Forum 29, 6, 1993–2003.Google ScholarCross Ref
10. Jakob, W., and Marschner, S. 2012. Manifold exploration: A Markov Chain Monte Carlo technique for rendering scenes with difficult specular transport. ACM Trans. Graph. 31, 4, 58:1–58:13. Google ScholarDigital Library
11. Jakob, W., 2012. Mitsuba v0.4. http://mitsuba-renderer.org.Google Scholar
12. 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 21, 3, 531–540.Google ScholarCross Ref
13. Levin, A., Zomet, A., Peleg, S., and Weiss, Y. 2004. Seamless image stitching in the gradient domain. In Proc. European Conference on Computer Vision (ECCV), 377–389.Google Scholar
14. MacKay, D. J. 2003. Information Theory, Inference, and Learning Algorithms. Cambridge University Press. Google ScholarDigital Library
15. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. 1953. Equation of state calculations by fast computing machines. Journal of Chemical Physics 21, 1087–1092.Google ScholarCross Ref
16. Overbeck, R., Donner, C., and Ramamoorthi, R. 2009. Adaptive wavelet rendering. ACM Trans. Graph. 28, 5, 140:1–140:12. Google ScholarDigital Library
17. Pérez, P., Gangnet, M., and Blake, A. 2003. Poisson image editing. ACM Trans. Graph. 22, 3, 313–318. Google ScholarDigital Library
18. Ramamoorthi, R., Mahajan, D., and Belhumeur, P. 2007. A first-order analysis of lighting, shading, and shadows. ACM Trans. Graph. 26, 1, 2:1–2:21. Google ScholarDigital Library
19. Rousselle, F., Knaus, C., and Zwicker, M. 2011. Adaptive sampling and reconstruction using greedy error minimization. ACM Trans. Graph. 30, 6, 159:1–159:12. Google ScholarDigital Library
20. Ruderman, D. 1994. The statistics of natural images. Network: computation in neural systems 5, 4, 517–548.Google Scholar
21. Soler, C., Subr, K., Durand, F., Holzschuch, N., and Sillion, F. 2009. Fourier depth of field. ACM Trans. Graph. 28, 2, 18:1–18:12. Google ScholarDigital Library
22. Tumblin, J., Agrawal, A., and Raskar, R. 2005. Why I want a gradient camera. In Proc. CVPR’05, 103–110. Google ScholarDigital Library
23. Veach, E., and Guibas, L. J. 1997. Metropolis light transport. In Proc. ACM SIGGRAPH 97, 65–76. Google ScholarDigital Library
24. Ward, G. J., and Heckbert, P. 1992. Irradiance gradients. In Proc. Eurographics Workshop on Rendering ’92.Google Scholar