“Adaptive rendering with linear predictions” by Moon, Iglesias-Guitian, Yoon and Mitchell

We propose a new adaptive rendering algorithm that enhances the performance of Monte Carlo ray tracing by reducing the noise, i.e., variance, while preserving a variety of high-frequency edges in rendered images through a novel prediction based reconstruction. To achieve our goal, we iteratively build multiple, but sparse linear models. Each linear model has its prediction window, where the linear model predicts the unknown ground truth image that can be generated with an infinite number of samples. Our method recursively estimates prediction errors introduced by linear predictions performed with different prediction windows, and selects an optimal prediction window minimizing the error for each linear model. Since each linear model predicts multiple pixels within its optimal prediction interval, we can construct our linear models only at a sparse set of pixels in the image screen. Predicting multiple pixels with a single linear model poses technical challenges, related to deriving error analysis for regions rather than pixels, and has not been addressed in the field. We address these technical challenges, and our method with robust error analysis leads to a drastically reduced reconstruction time even with higher rendering quality, compared to state-of-the-art adaptive methods. We have demonstrated that our method outperforms previous methods numerically and visually with high performance ray tracing kernels such as OptiX and Embree.

1. Allen, D. M. 1974. The relationship between variable selection and data agumentation and a method for prediction. Technometrics 16, 1, 125–127.Google ScholarCross Ref
2. Bauszat, P., Eisemann, M., and Magnor, M. 2011. Guided image filtering for interactive high-quality global illumination. Computer Graphics Forum 30, 4, 1361–1368. Google ScholarDigital Library
3. Dammertz, H., Sewtz, D., Hanika, J., and Lensch, H. P. A. 2010. Edge-avoiding A-Trous wavelet transform for fast global illumination filtering. In High Performance Graphics, 67–75. Google ScholarDigital Library
4. Delbracio, M., Musé, P., Buades, A., Chauvier, J., Phelps, N., and Morel, J.-M. 2014. Boosting Monte Carlo rendering by ray histogram fusion. ACM Trans. Graph. 33, 1, 8:1–8:15. Google ScholarDigital Library
5. 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, 3, 93:1–93:13. Google ScholarDigital Library
6. Egan, K., Durand, F., and Ramamoorthi, R. 2011. Practical filtering for efficient ray-traced directional occlusion. ACM Trans. Graph. 30, 6, 180:1–180:10. Google ScholarDigital Library
7. Egan, K., Hecht, F., Durand, F., and Ramamoorthi, R. 2011. Frequency analysis and sheared filtering for shadow light fields of complex occluders. ACM Trans. Graph. 30, 2, 9:1–9:13. Google ScholarDigital Library
8. 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
9. Kajiya, J. T. 1986. The rendering equation. In ACM SIGGRAPH ’86, 143–150. Google ScholarDigital Library
10. Kalantari, N. K., and Sen, P. 2013. Removing the noise in Monte Carlo rendering with general image denoising algorithms. Computer Graphics Forum 32, 2pt1, 93–102.Google Scholar
11. Kohavi, R. 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. In 14th International Joint Conference on Artificial Intelligence, 1137–1143. Google ScholarDigital Library
12. Křivánek, J., Bouatouch, K., Pattanaik, S. N., and Žára, J. 2006. Making radiance and irradiance caching practical: Adaptive caching and neighbor clamping. In Eurographics Symposium on Rendering, 127–138. Google ScholarDigital Library
13. Lehtinen, J., Aila, T., Chen, J., Laine, S., and Durand, F. 2011. Temporal light field reconstruction for rendering distribution effects. ACM Trans. Graph. 30, 4, 55:1–55:12. Google ScholarDigital Library
14. Lehtinen, J., Aila, T., Laine, S., and Durand, F. 2012. Reconstructing the indirect light field for global illumination. ACM Trans. Graph. 31, 4, 51:1–51:10. Google ScholarDigital Library
15. Li, T.-M., Wu, Y.-T., and Chuang, Y.-Y. 2012. Sure-based optimization for adaptive sampling and reconstruction. ACM Trans. Graph. 31, 6, 194:1–194:9. Google ScholarDigital Library
16. Ljung, L., and SöderströM, T. 1987. Theory and practice of recursive identification. MIT Press.Google Scholar
17. Mitchell, D. P. 1987. Generating antialiased images at low sampling densities. In ACM SIGGRAPH ’87, vol. 21, 65–72. Google ScholarDigital Library
18. Moon, B., Jun, J. Y., Lee, J., Kim, K., Hachisuka, T., and Yoon, S.-E. 2013. Robust image denoising using a virtual flash image for Monte Carlo ray tracing. Computer Graphics Forum 32, 1, 139–151.Google ScholarCross Ref
19. Moon, B., Carr, N., and Yoon, S.-E. 2014. Adaptive rendering based on weighted local regression. ACM Trans. Graph. 33, 5, 170. Google ScholarDigital Library
20. Overbeck, R. S., Donner, C., and Ramamoorthi, R. 2009. Adaptive wavelet rendering. ACM Trans. Graph. 28, 5, 140:1–140:12. Google ScholarDigital Library
21. Parker, S. G., Bigler, J., Dietrich, A., Friedrich, H., Hoberock, J., Luebke, D., McAllister, D., McGuire, M., Morley, K., Robison, A., and Stich, M. 2010. Optix: A general purpose ray tracing engine. ACM Trans. Graph. 29, 4, 66:1–66:13. Google ScholarDigital Library
22. Pharr, M., and Humphreys, G. 2010. Physically Based Rendering: From Theory to Implementation 2nd. Morgan Kaufmann Publishers Inc. Google ScholarDigital Library
23. 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
24. Rousselle, F., Knaus, C., and Zwicker, M. 2012. Adaptive rendering with non-local means filtering. ACM Trans. Graph. 31, 6, 195:1–195:11. Google ScholarDigital Library
25. Rousselle, F., Manzi, M., and Zwicker, M. 2013. Robust denoising using feature and color information. Computer Graphics Forum 32, 7, 121–130.Google ScholarCross Ref
26. 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
27. Wald, I., Woop, S., Benthin, C., Johnson, G. S., and Ernst, M. 2014. Embree: A kernel framework for efficient cpu ray tracing. ACM Trans. Graph. 33, 4, 143:1–143:8. Google ScholarDigital Library
28. Ward, G. J., Rubinstein, F. M., and Clear, R. D. 1988. A ray tracing solution for diffuse interreflection. In ACM SIGGRAPH ’88, vol. 22, 85–92. Google ScholarDigital Library