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Issue No.10 - October (2011 vol.17)

pp: 1499-1509

Liang Wan , Tianjin University, Tianjin, City University of Hong Kong, Hong Kong, and The Chinese University of Hong Kong, Hong Kong

Shue-Kwan Mak , City University of Hong Kong, Hong Kong

Tien-Tsin Wong , Chinese University of Hong Kong, Hong Kong

Chi-Sing Leung , City University of Hong Kong, Hong Kong

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2011.31

ABSTRACT

Environment sampling is a popular technique for rendering scenes with distant environment illumination. However, the temporal consistency of animations synthesized under dynamic environment sequences has not been fully studied. This paper addresses this problem and proposes a novel method, namely spatiotemporal sampling, to fully exploit both the temporal and spatial coherence of environment sequences. Our method treats an environment sequence as a spatiotemporal volume and samples the sequence by stratifying the volume adaptively. For this purpose, we first present a new metric to measure the importance of each stratified volume. A stratification algorithm is then proposed to adaptively suppress the abrupt temporal and spatial changes in the generated sampling patterns. The proposed method is able to automatically adjust the number of samples for each environment frame and produce temporally coherent sampling patterns. Comparative experiments demonstrate the capability of our method to produce smooth and consistent animations under dynamic environment sequences.

INDEX TERMS

Spatiotemporal sampling, dynamic environment sequences, temporal consistency, importance metric, adaptive volume stratification.

CITATION

Liang Wan, Shue-Kwan Mak, Tien-Tsin Wong, Chi-Sing Leung, "Spatiotemporal Sampling of Dynamic Environment Sequences",

*IEEE Transactions on Visualization & Computer Graphics*, vol.17, no. 10, pp. 1499-1509, October 2011, doi:10.1109/TVCG.2011.31REFERENCES

- [1] J.F. Blinn and M.E. Newell, “Texture and Reflection in Computer Generated Images,”
Comm. ACM, vol. 19, no. 10, pp. 542-546, Oct. 1976.- [2] N. Greene, “Environment Mapping and Other Applications of World Projections,”
IEEE Computer Graphics and Applications, vol. CGA-6, no. 11, pp. 21-29, Nov. 1986.- [3] P. Debevec, “Rendering Synthetic Objects into Real Scenes: Bridging Traditional and Image-Based Graphics with Global Illumination and High Dynamic Range Photography,”
Proc. ACM SIGGRAPH, pp. 189-198, 1998.- [4] P.E. Debevec and J. Malik, “Recovering High Dynamic Range Radiance Maps from Photographs,”
Proc. ACM SIGGRAPH, pp. 369-378, 1997.- [5] S. Agarwal, R. Ramamoorthi, S. Belongie, and H.W. Jensen, “Structured Importance Sampling of Environment Maps,”
ACM Trans. Graphics, vol. 22, no. 3, pp. 605-612, 2003.- [6] T. Kollig and A. Keller, “Efficient Illumination by High Dynamic Range Images,”
Proc. 14th Eurographics Workshop Rendering, pp. 45-51, 2003.- [7] V. Ostromoukhov, C. Donohue, and P.M. Jodoin, “Fast Hierarchical Importance Sampling with Blue Noise Properties,”
ACM Trans. Graphics, vol. 23, no. 3, pp. 488-495, 2004.- [8] P. Debevec, “A Median Cut Algorithm for Light Probe Sampling,”
Proc. ACM SIGGRAPH, 2005.- [9] L. Wan, T.-T. Wong, and C.-S. Leung, “Spherical Q2-Tree for Sampling Dynamic Environment Sequences,”
Proc. Eurographics Symp. Rendering, pp. 21-30, 2005.- [10] V. Havran, M. Smyk, G. Krawczyk, K. Myszkowski, and H.-P. Seidel, “Interactive System for Dynamic Scene Lighting Using Captured Video Environment Maps,”
Proc. Eurographics Symp. Rendering, pp. 31-42, 2005.- [11] C.B. Madsen, M.K.D. Sørensen, and M. Vittrup, “Estimating Positions and Radiances of a Small Number of Light Sources for Real-Time Image-Based Lighting,”
Proc. Ann. Conf. European Assoc. for Computer Graphics (EUROGRAPHICS), pp. 37-44, 2003.- [12] P. Shirley, “Physically Based Lighting Calculations for Computer Graphics,” PhD Dissertation, Univ. of Illi nois, 1990.
- [13] M.D. McCool and P.K. Harwood, “Probability Trees,”
Proc. Graphics Interface, pp. 37-46, 1997.- [14] J. Lawrence, S. Rusinkiewicz, and R. Ramamoorthi, “Efficient BRDF Importance Sampling Using a Factored Representation,”
ACM Trans. Graphics, vol. 23, no. 3, pp. 496-505, 2004.- [15] D. Burke, A. Ghosh, and W. Heidrich, “Bidirectional Importance Sampling for Direct Illumination,”
Proc. Eurographics Symp. Rendering, pp. 147-156, 2005.- [16] P. Clarberg, W. Jarosz, T. Akenine-Möller, and H.W. Jensen, “Wavelet Importance Sampling: Efficiently Evaluating Products of Complex Functions,”
Proc. ACM SIGGRAPH, pp. 1166-1175, 2005.- [17] A. Ghosh, A. Doucet, and W. Heidrich, “Sequential Sampling for Dynamic Environment Map Illumination,”
Proc. Eurographics Symp. Rendering, pp. 115-126, 2006.- [18] P. Clarberg and T. Akenine-Möller, “Practical Product Importance Sampling for Direct Illumination,”
Computer Graphics Forum, vol. 27, no. 2, pp. 681-690, 2008.- [19] W. Jarosz, N.A. Carr, and H.W. Jensen, “Importance Sampling Spherical Harmonics,”
Computer Graphics Forum, vol. 28, no. 2, pp. 577-586, 2009.- [20] S. Gibson and A. Murta, “Interactive Rendering with Real-World Illumination,”
Proc. Eurographics Workshop Rendering Techniques, pp. 365-376, 2000.- [21] T. Annen, Z. Dong, T. Mertens, P. Bekaert, H.-P. Seidel, and J. Kautz, “Real-Time, All-Frequency Shadows in Dynamic Scenes,”
ACM Trans. Graphics, vol. 27, no. 3, pp. 1-8, 2008.- [22] M. Hasan, E. Velázquez-Armendáriz, F. Pellacini, and K. Bala, “Tensor Clustering for Rendering Many-Light Animations,”
Computer Graphics Forum, vol. 27, no. 4, pp. 1105-1114, 2008.- [23] K.M. Gorski, B.D. Wandelt, E. Hivon, F.K. Hansen, and A.J. Banday, “The HEALPix Primer,” Technical Report astro-ph/9905275, Theoretical Astrophysics Center (TAC), Feb. 2003.
- [24] D. Cline, P.K. Egbert, J. Talbot, and D. Cardon, “Two Stage Importance Sampling for Direct Lighting,”
Proc. Eurographics Symp. Rendering, pp. 103-113, 2006.- [25] F.C. Crow, “Summed-Area Tables for Texture Mapping,”
Proc. ACM SIGGRAPH, pp. 207-212, 1984.- [26] “Ladybug2,” Point Grey Research http://www.ptgrey.com/ productsladybug2 /, 2006.
- [27] G.J. Ward, “Measuring and Modeling Anisotropic Reflection,”
SIGGRAPH Computer Graphics, vol. 26, no. 2, pp. 265-272, 1992.- [28] W. Feng, J. Jia, and Z.-Q. Liu, “Self-Validated Labeling of Markov Random Fields for Image Segmentation,”
IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 10, pp. 1871-1887, Oct. 2010. |