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Animation rendering with Population Monte Carlo image-plane sampler

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Abstract

Except for the first frame, a population Monte Carlo image plane (PMC-IP) sampler renders with a start-up kernel function learned from previous results by using motion analysis techniques in the vision community to explore the temporal coherence existing among kernel functions. The predicted kernel function can shift part of the uniformly distributed samples from regions with low visual variance to regions with high visual variance at the start-up iteration and reduce the temporal noise by considering the temporal relation of sample distributions among frames. In the following iterations, the PMC-IP sampler adapts the kernel function to select pixels for refinement according to a perceptually-weighted variance criterion. Our results improve the rendering efficiency by a factor between 2 to 5 over existing techniques in single frame rendering. The rendered animations are perceptually more pleasant.

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References

  1. Aydin, T., Mantiuk, R., Myszkowski, K., Seidel, H.: (2008)

  2. Baker, S., Black, M.J., Lewis, J., Roth, S., Scharstein, D., Szeliski, R.: A database and evaluation methodology for optical flow. In: IEEE ICCV (2007)

  3. Bolin, M.R., Meyer, G.W.: A perceptually based adaptive sampling algorithm. In: SIGGRAPH ’98, pp. 299–309 (1998)

  4. Dayal, A., Woolley, C., Watson, B., Luebke, D.: Adaptive frameless rendering. In: Proc. of the 16th Eurographics Symposium on Rendering, pp. 265–275 (2005)

  5. Dippé, M.A.Z., Wold, E.H.: Antialiasing through stochastic sampling. In: SIGGRAPH ’85, pp. 69–78 (1985)

  6. Douc, R., Guillin, A., Marin, J.M., Robert, C.P.: Convergence of adaptive sampling schemes. Technical Report 2005–2006, University Paris Dauphine (2005). http://www.cmap.polytechnique.fr/~douc/Page/Research/dgmr.pdf

  7. Farrugia, J.P., Péroche, B.: A progressive rendering algorithm using an adaptive pereptually based image metric. Comput. Graph. Forum (Proc. Eurograph. 2004) 23(3), 605–614 (2004)

    Article  Google Scholar 

  8. Ferwerda, J.A., Pattanaik, S.N., Shirley, P., Greenberg, D.P.: A model of visual adaptation for realistic image synthesis. In: SIGGRAPH ’96, pp. 249–258 (1996)

  9. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981). doi:10.1145/358669.358692

    Article  MathSciNet  Google Scholar 

  10. Ghosh, A., Doucet, A., Heidrich, W.: Sequential sampling for dynamic environment map illumination. In: Proc. Eurographics Symposium on Rendering, pp. 115–126 (2006)

  11. Glassner, A.: Principles of Digital Image Synthesis. Morgan Kaufmann, San Mateo (1995)

    Google Scholar 

  12. Hesterberg, T.: Weighted average importance sampling and defensive mixture distributions. Technometrics 37, 185–194 (1995)

    Article  MATH  Google Scholar 

  13. Kirk, D., Arvo, J.: Unbiased sampling techniques for image synthesis. In: SIGGRAPH ’91, pp. 153–156 (1991)

  14. Lai, Y., Fan, S., Chenney, S., Dyer, C.: Photorealistic image rendering with population Monte Carlo energy redistribution. In: Eurographics Symposium on Rendering, pp. 287–296 (2007)

  15. Lai, Y.C., Liu, F., Zhang, L., Dyer, C.: Efficient schemes for Monte Carlo Markov chain algorithms in global illumination. In: ISVC ’08: Proceedings of the 4th International Symposium on Advances in Visual Computing, pp. 614–623 (2008)

  16. Lee, M.E., Redner, R.A., Uselton, S.P.: Statistically optimized sampling for distributed ray tracing. In: SIGGRAPH ’85, pp. 61–68 (1985)

  17. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  18. Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proc. of International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)

  19. Mantiuk, R., Myszkowski, K., Seidel, H.P.: Visible difference predicator for high dynamic range images. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp. 2763–2769 (2004)

  20. Mitchell, D.P.: Generating antialiased images at low sampling densities. In: SIGGRAPH ’87, pp. 65–72 (1987)

  21. Painter, J., Sloan, K.: Antialiased ray tracing by adaptive progressive refinement. In: SIGGRAPH ’89, pp. 281–288 (1989)

  22. Pharr, M., Humphreys, G.: Physically Based Rendering from Theory to Implementation. Morgan Kaufmann, San Mateo (2004)

    Google Scholar 

  23. Purgathofer, W.: A statistical method for adaptive stochastic sampling. In: Proc. EUROGRAPHICS 86, pp. 145–152 (1986)

  24. Ramasubramanian, M., Pattanaik, S.N., Greenberg, D.P.: A perceptually based physical error metric for realistic image synthesis. In: SIGGRAPH ’99, pp. 73–82 (1999)

  25. Rigau, J., Feixas, M., Sbert, M.: New contrast measures for pixel supersampling. In: Proc. of CGI’02, pp. 439–451. Springer, Berlin (2002)

    Google Scholar 

  26. Rigau, J., Feixas, M., Sbert, M.: Entropy-based adaptive sampling. In: Proc. of Graphics Interface 2003, pp. 149–157 (2003)

  27. Schlick, C.: An adaptive sampling technique for multidimensional integration by ray-tracing. In: Proc. of the 2nd Eurographics Workshop on Rendering, pp. 21–29 (1991)

  28. Stokes, W.A., Ferwerda, J.A., Walter, B., Greenberg, D.P.: Perceptual illumination components: a new approach to efficient, high quality global illumination rendering. In: SIGGRAPH ’04, pp. 742–749 (2004)

  29. Szeliski, R.: Image alignment and stitching: A tutorial. Tech. Rep. MSR-TR-2004-92, Microsoft Research (2006)

  30. Tamstorf, R., Jensen, H.W.: Adaptive sampling and bias estimation in path tracing. In: Proc. of the 8th Eurographics Workshop on Rendering, pp. 285–296 (1997)

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Authors and Affiliations

  1. National Taiwan University of Science and Technology, Taipei, ROC

    Yu-Chi Lai

  2. Emergent Game Technology, Chapel Hill, USA

    Stephen Chenney

  3. University of Wisconsin-Madison, Madison, USA

    Feng Liu

  4. Shandong University, Jinan, China

    Yuzhen Niu

  5. Suzhou University Financial Engineering Research Center, Suzhou, China

    Shaohua Fan

  6. NYU Courant Institute of Mathematical Sciences, New York, USA

    Shaohua Fan

Authors
  1. Yu-Chi Lai
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  2. Stephen Chenney
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  3. Feng Liu
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  4. Yuzhen Niu
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  5. Shaohua Fan
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Corresponding author

Correspondence to Shaohua Fan.

Additional information

Y.-C. Lai funded by: NSC 99-2218-E-011-005-, Taiwan.

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Lai, YC., Chenney, S., Liu, F. et al. Animation rendering with Population Monte Carlo image-plane sampler. Vis Comput 26, 543–553 (2010). https://doi.org/10.1007/s00371-010-0503-5

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