Adaptive Rendering based on Adaptive Order Selection

Hongliang Yuan, Changwen Zheng, Quan Zheng, Yu Liu

Abstract

We propose a new adaptive sampling and reconstruction method based on a novel, adaptive order polynomial fitting which can preserve various high-frequency features in generated images and meanwhile mitigate the noise efficiently. Some auxiliary features have strong linear correlation with luminance intensity in the smooth regions of the image, but the relationship does not hold in the high-frequency regions. In order to handle these cases robustly, we approximate luminance intensity in the auxiliary feature space by constructing local polynomial functions with order varying adaptively. Firstly, we sample the image space uniformly. Then we decide the order of fitting with the least estimated mean squared error (MSE) for each pixel. Finally, we distribute additional ray samples to areas with higher estimated MSE if sampling budget remains. We demonstrate that our method makes significant improvement in terms of both numerical error and visual quality compared with the state-of-the-art.

References

  1. Bitterli, B., Rousselle, F., Moon, B., Guitián, J. A. I., Adler, D., Mitchell, K., Jarosz, W., and Novák, J. (2016). Nonlinearly weighted first-order regression for denoising Monte Carlo renderings. Comput. Graph. Forum, 35(4):107-117.
  2. Buades, A., Coll, B., and Morel, J.-M. (2008). Nonlocal image and movie denoising. Int. J. Comput. Vision, 76(2):123-139.
  3. 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.
  4. Dobkin, D. P., Eppstein, D., and Mitchell, D. P. (1996). Computing the discrepancy with applications to supersampling patterns. ACM Trans. Graph., 15(4):354-376.
  5. Donoho, D. L. and Johnstone, I. M. (1994). Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81(3):425-455.
  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. In ACM SIGGRAPH 2008 Papers, SIGGRAPH 7808, pages 33:1-33:10, New York. ACM.
  7. Jianqing Fan, I. G. (1995a). Adaptive order polynomial fitting: Bandwidth robustification and bias reduction. Journal of Computational and Graphical Statistics, 4(3):213-227.
  8. Jianqing Fan, I. G. (1995b). Data-driven bandwidth selection in local polynomial fitting: Variable bandwidth and spatial adaptation. Journal of the Royal Statistical Society. Series B (Methodological), 57(2):371-394.
  9. Kajiya, J. T. (1986). The rendering equation. In Proceedings of the 13th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 7886, pages 143-150, New York. ACM.
  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.
  11. Li, T.-M., Wu, Y.-T., and Chuang, Y.-Y. (2012). SUREbased optimization for adaptive sampling and reconstruction. ACM Trans. Graph., 31(6):194:1-194:9.
  12. Mitchell, D. P. (1987). Generating antialiased images at low sampling densities. In Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 7887, pages 65-72, New York. ACM.
  13. Moon, B., Carr, N., and Yoon, S.-E. (2014). Adaptive rendering based on weighted local regression. ACM Trans. Graph., 33(5):170:1-170:14.
  14. 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.
  15. Moon, B., Jun, J. Y., Lee, J., Kim, K., Hachisuka, T., and Yoon, S. (2013). Robust image denoising using a virtual flash image for monte carlo ray tracing. Comput. Graph. Forum, 32(1):139-151.
  16. Moon, B., McDonagh, S., Mitchell, K., and Gross, M. (2016). Adaptive polynomial rendering. ACM Trans. Graph., 35(4):40:1-40:10.
  17. Nadaraya, E. A. (1964). On estimating regression. Theory of Probability & Its Applications, 9(1):141-142.
  18. Overbeck, R. S., Donner, C., and Ramamoorthi, R. (2009). Adaptive wavelet rendering. In ACM SIGGRAPH Asia 2009 Papers, SIGGRAPH Asia 7809, pages 140:1- 140:12, New York. ACM.
  19. Pharr, M. and Humphreys, G. (2010). Physically Based Rendering: From Theory to Implementation. Morgan Kaufmann Publishers Inc., San Francisco.
  20. 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.
  21. Rousselle, F., Knaus, C., and Zwicker, M. (2012). Adaptive rendering with non-local means filtering. ACM Trans. Graph., 31(6):195:1-195:11.
  22. Rousselle, F., Manzi, M., and Zwicker, M. (2013). Robust denoising using feature and color information. Comput. Graph. Forum, 32(7):121-130.
  23. 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.
  24. Stein, C. M. (1981). Estimation of the mean of a multivariate normal distribution. The Annals of Statistics, 9(6):1135-1151.
  25. Tomasi, C. and Manduchi, R. (1998). Bilateral filtering for gray and color images. In Proceedings of the Sixth International Conference on Computer Vision, ICCV 7898, pages 839-, Washington, DC, USA. IEEE Computer Society.
  26. Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. Trans. Img. Proc., 13(4):600-612.
  27. Watson, G. S. (1964). Smooth regression analysis. Sankhy: The Indian Journal of Statistics, Series A (1961-2002), 26(4):359-372.
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Paper Citation


in Harvard Style

Yuan H., Zheng C., Zheng Q. and Liu Y. (2017). Adaptive Rendering based on Adaptive Order Selection . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2017) ISBN 978-989-758-224-0, pages 37-45. DOI: 10.5220/0006093100370045


in Bibtex Style

@conference{grapp17,
author={Hongliang Yuan and Changwen Zheng and Quan Zheng and Yu Liu},
title={Adaptive Rendering based on Adaptive Order Selection},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2017)},
year={2017},
pages={37-45},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006093100370045},
isbn={978-989-758-224-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2017)
TI - Adaptive Rendering based on Adaptive Order Selection
SN - 978-989-758-224-0
AU - Yuan H.
AU - Zheng C.
AU - Zheng Q.
AU - Liu Y.
PY - 2017
SP - 37
EP - 45
DO - 10.5220/0006093100370045