Fabrice Rousselle
Matthias Zwicker
ABSTRACT
We introduce a novel approach for image space adaptive sampling and reconstruction in Monte Carlo rendering. We greedily minimize relative mean squared error (MSE) by iterating over two steps. First, given a current sample distribution, we optimize over a discrete set of filters at each pixel and select the filter that minimizes the pixel error. Next, given the current filter selection, we distribute additional samples to further reduce MSE. The success of our approach hinges on a robust technique to select suitable per pixel filters. We develop a novel filter selection procedure that robustly solves this problem even with noisy input data. We evaluate our approach using effects such as motion blur, depth of field, interreflections, etc. We provide a comparison to a state-of-the-art algorithm based on wavelet shrinkage and show that we achieve significant improvements in numerical error and visual image quality. Our approach is simple to implement, requires a single user parameter, and is compatible with standard Monte Carlo rendering.
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- Bala, K., Walter, B., and Greenberg, D. P. 2003. Combining edges and points for interactive high-quality rendering. ACM Trans. Graph. 22 (July), 631--640. Google Scholar
Digital Library
- Bolin, M. R., and Meyer, G. W. 1998. A perceptually based adaptive sampling algorithm. In SIGGRAPH '98, 299--309. Google ScholarDigital Library
- Chen, J., Wang, B., Wang, Y., Overbeck, R. S., Yong, J.-H., and Wang, W. 2011. Efficient depth-of-field rendering with adaptive sampling and multiscale reconstruction. Computer Graphics Forum.Google Scholar
- Dammertz, H., Sewtz, D., Hanika, J., and Lensch, H. P. A. 2010. Edge-avoiding à-trous wavelet transform for fast global illumination filtering. In Proceedings of the Conference on High Performance Graphics, Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, HPG '10, 67--75. Google ScholarDigital Library
- Deussen, O., Hanrahan, P., Lintermann, B., Měch, R., Pharr, M., and Prusinkiewicz, P. 1998. Realistic modeling and rendering of plant ecosystems. In Proceedings of the 25th annual conference on Computer graphics and interactive techniques, ACM, New York, NY, USA, SIGGRAPH '98, 275--286. Google ScholarDigital Library
- Donoho, D. L., and Johnstone, J. M. 1994. Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 3, 425--455.Google ScholarCross Ref
- Egan, K., Tseng, Y.-T., Holzschuch, N., Durand, F., and Ramamoorthi, R. 2009. Frequency analysis and sheared reconstruction for rendering motion blur. ACM Trans. Graph. 28 (July), 93:1--93:13. Google ScholarDigital Library
- Egan, K., Hecht, F., Durand, F., and Ramamoorthi, R. 2011. Frequency analysis and sheared filtering for shadow light fields of complex occluders. ACM Transactions on Graphics 30, 2 (Apr.), 9:1--9:13. Google ScholarDigital Library
- Farrugia, J., and Péroche, B. 2004. A progressive rendering algorithm using an adaptive perceptually based image metric. In Computer Graphics Forum, vol. 23, Wiley Online Library, 605--614.Google Scholar
- Goldenshluger, A., and Nemirovski, A. 1997. On spatial adaptive estimation of nonparametric regression. Math. Meth. Statistics 6, 135--170.Google Scholar
- 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 (August), 33:1--33:10. Google ScholarDigital Library
- Katkovnik, V. 1999. A new method for varying adaptive bandwidth selection. Signal Processing, IEEE Transactions on 47, 9, 2567--2571. Google ScholarDigital Library
- Kollig, T., and Keller, A. 2002. Efficient multidimensional sampling. Computer Graphics Forum 21, 3, 557--563.Google ScholarCross Ref
- Lee, J.-S. 1980. Digital image enhancement and noise filtering by use of local statistics. IEEE Trans. PAMI 2, 2 (march), 165--168. Google ScholarDigital Library
- Lehtinen, J., Aila, T., Chen, J., Laine, S., and Durand, F. 2011. Temporal light field reconstruction for rendering distribution effects. ACM Trans. Graph. 30 (August), 55:1--55:12. Google ScholarDigital Library
- Lepski, O. V., Mammen, E., and Spokoiny, V. G. 1997. Optimal spatial adaptation to inhomogeneous smoothness: An approach based on kernel estimates with variable bandwidth selectors. The Annals of Statistics 25, 3, pp. 929--947.Google ScholarCross Ref
- Mitchell, D. P. 1987. Generating antialiased images at low sampling densities. SIGGRAPH Comput. Graph. 21 (August), 65--72. Google ScholarDigital Library
- Overbeck, R. S., Donner, C., and Ramamoorthi, R. 2009. Adaptive wavelet rendering. ACM Trans. Graph. 28 (December), 140:1--140:12. Google ScholarDigital Library
- Pharr, M., and Humphreys, G. 2010. Physically Based Rendering, Second Edition: From Theory To Implementation, 2nd ed. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA. Google ScholarDigital Library
- Portilla, J., Strela, V., Wainwright, M., and Simoncelli, E. 2003. Image denoising using scale mixtures of gaussians in the wavelet domain. Image Processing, IEEE Transactions on 12, 11, 1338--1351. Google ScholarDigital Library
- Ritschel, T., Engelhardt, T., Grosch, T., Seidel, H.-P., Kautz, J., and Dachsbacher, C. 2009. Micro-rendering for scalable, parallel final gathering. ACM Trans. Graph. 28 (December), 132:1--132:8. Google ScholarDigital Library
- Shirley, P., Aila, T., Cohen, J., Enderton, E., Laine, S., Luebke, D., and McGuire, M. 2011. A local image reconstruction algorithm for stochastic rendering. In Symposium on Interactive 3D Graphics and Games, ACM, New York, NY, USA, I3D '11, 9--14 PAGE@5. Google ScholarDigital Library
- Silverman, B. 1986. Density estimation for statistics and data analysis, vol. 26. Chapman & Hall/CRC.Google Scholar
- Soler, C., Subr, K., Durand, F., Holzschuch, N., and Sillion, F. 2009. Fourier depth of field. ACM Trans. Graph. 28 (May), 18:1--18:12. Google ScholarDigital Library
Index Terms
- Adaptive sampling and reconstruction using greedy error minimization
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