Name
Abstract | ||
|---|---|---|
Transmission of radiation through spatially-correlated media has demonstrated deviations from the classical exponential law of the corresponding uncorrelated media. In this paper, we propose a general, physically-based method for modeling such correlated media with non-exponential decay of transmittance. We describe spatial correlations by introducing the Fractional Gaussian Field (FGF), a powerful mathematical tool that has proven useful in many areas but remains under-explored in graphics. With the FGF, we study the effects of correlations in a unified manner, by modeling both high-frequency, noise-like fluctuations and k-th order fractional Brownian motion (fBm) with a stochastic continuity property. As a result, we are able to reproduce a wide variety of appearances stemming from different types of spatial correlations. Compared to previous work, our method is the first that addresses both short-range and long-range correlations using physically-based fluctuation models. We show that our method can simulate different extents of randomness in spatially-correlated media, resulting in a smooth transition in a range of appearances from exponential falloff to complete transparency. We further demonstrate how our method can be integrated into an energy-conserving RTE framework with a well-designed importance sampling scheme and validate its ability compared to the classical transport theory and previous work.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3306346.3323031 | ACM Transactions on Graphics (TOG) |
Keywords | Field | DocType |
correlation, importance sampling, non-exponential transmittance, random field, volume rendering | Statistical physics,Graphics,Mathematical optimization,Importance sampling,Random field,Exponential function,Gaussian,Rendering (computer graphics),Fractional Brownian motion,Mathematics,Randomness | Journal |
Volume | Issue | ISSN |
38 | 4 | 0730-0301 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Guo | 1 | 289 | 56.10 |
| Yanjun Chen | 2 | 16 | 7.26 |
| Bingyang Hu | 3 | 4 | 2.11 |
| Ling-Qi Yan | 4 | 12 | 7.99 |
| Yan-Wen Guo | 5 | 348 | 39.32 |
| Yuntao Liu | 6 | 4 | 5.55 |