“Adaptive polynomial rendering”

  • ©Bochang Moon, Steven McDonagh, Kenny Mitchell, and Markus Gross

Conference:


Type:


Title:

    Adaptive polynomial rendering

Session/Category Title: EFFICIENT SAMPLING & RENDERING

Presenter(s)/Author(s):


Moderator(s):


Abstract:


    In this paper, we propose a new adaptive rendering method to improve the performance of Monte Carlo ray tracing, by reducing noise contained in rendered images while preserving high-frequency edges. Our method locally approximates an image with polynomial functions and the optimal order of each polynomial function is estimated so that our reconstruction error can be minimized. To robustly estimate the optimal order, we propose a multi-stage error estimation process that iteratively estimates our reconstruction error. In addition, we present an energy-preserving outlier removal technique to remove spike noise without causing noticeable energy loss in our reconstruction result. Also, we adaptively allocate additional ray samples to high error regions guided by our error estimation. We demonstrate that our approach outperforms state-of-the-art methods by controlling the tradeoff between reconstruction bias and variance through locally defining our polynomial order, even without need for filtering bandwidth optimization, the common approach of other recent methods.

References:


    1. 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
    2. Durand, F., Holzschuch, N., Soler, C., Chan, E., and Sillion, F. X. 2005. A frequency analysis of light transport. ACM Trans. Graph. 24, 3, 1115–1126. Google ScholarDigital Library
    3. 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
    4. 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
    5. 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
    6. 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
    7. Hayter, A. 2007. Probability and statistics for engineers and scientists 3rd. Brooks/Cole Publishing Co.Google Scholar
    8. Kajiya, J. T. 1986. The rendering equation. In ACM SIGGRAPH ’86, 143–150. Google ScholarDigital Library
    9. 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
    10. Kalantari, N. K., Bako, S., and Sen, P. 2015. A machine learning approach for filtering Monte Carlo noise. ACM Trans. Graph. 34, 4, 122:1–122:12. Google ScholarDigital Library
    11. 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
    12. 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
    13. 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
    14. McCool, M. D. 1999. Anisotropic diffusion for Monte Carlo noise reduction. ACM Trans. Graph. 18, 2, 171–194. Google ScholarDigital Library
    15. Mehta, S. U., Wang, B., and Ramamoorthi, R. 2012. Axis-aligned filtering for interactive sampled soft shadows. ACM Trans. Graph. 31, 6, 163:1–163:10. Google ScholarDigital Library
    16. Mehta, S. U., Wang, B., Ramamoorthi, R., and Durand, F. 2013. Axis-aligned filtering for interactive physically-based diffuse indirect lighting. ACM Trans. Graph. 32, 4, 96:1–96:12. Google ScholarDigital Library
    17. Mehta, S. U., Yao, J., Ramamoorthi, R., and Durand, F. 2014. Factored axis-aligned filtering for rendering multiple distribution effects. ACM Trans. Graph. 33, 4, 57:1–57:12. 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. Moon, B., Iglesias-Guitian, J. A., Yoon, S.-E., and Mitchell, K. 2015. Adaptive rendering with linear predictions. ACM Trans. Graph. 34, 4, 121:1–121:11. Google ScholarDigital Library
    21. Overbeck, R. S., Donner, C., and Ramamoorthi, R. 2009. Adaptive wavelet rendering. ACM Trans. Graph. 28, 5, 140:1–140:12. 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. Ruppert, D., and Wand, M. 1994. Multivariate locally weighted least squares regression. The annals of statistics 22, 3, 1346–1370.Google Scholar
    27. Rushmeier, H. E., and Ward, G. J. 1994. Energy preserving non-linear filters. In ACM SIGGRAPH, 131–138. Google ScholarDigital Library
    28. Sen, P., and Darabi, S. 2012. On filtering the noise from the random parameters in Monte Carlo rendering. ACM Trans. Graph. 31, 3, 18:1–18:15. Google ScholarDigital Library
    29. 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
    30. Yan, L.-Q., Mehta, S. U., Ramamoorthi, R., and Durand, F. 2015. Fast 4d sheared filtering for interactive rendering of distribution effects. ACM Trans. Graph. 35, 1, 7:1–7:13. Google ScholarDigital Library
    31. Zwicker, M., Jarosz, W., Lehtinen, J., Moon, B., Ramamoorthi, R., Rousselle, F., Sen, P., Soler, C., and Yoon, S.-E. 2015. Recent advances in adaptive sampling and reconstruction for Monte Carlo rendering. Computer Graphics Forum 34, 2, 667–681. Google ScholarDigital Library

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