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Volume Scattering Probability Guiding
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AI
DOI：10.1145/3687982.png)
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Abstract
En 中文
Simulating the light transport of volumetric effects poses significant chal-lenges and costs, especially in the presence of heterogeneous volumes. Gen-erating stochastic paths for volume rendering involves multiple decisions,and previous works mainly focused on directional and distance sampling,where the volume scattering probability (VSP), i.e., the probability of scat-tering inside a volume, is indirectly determined as a byproduct of distancesampling. We demonstrate that direct control over the VSP can significantlyimprove efficiency and present an unbiased volume rendering algorithmbased on an existing resampling framework for precise control over the VSP.Compared to previous state-of-the-art, which can only increase the VSPwithout guaranteeing to reach the desired value, our method also supportsdecreasing the VSP. We further present a data-driven guiding frameworkto efficiently learn and query an approximation of the optimal VSP every-where in the scene without the need for user control. Our approach can easily be combined with existing path-guiding methods for directional sam-pling at minimal overhead and shows significant improvements over thestate-of-the-art in various complex volumetric lighting scenarios.
Keywords:
scattering probability guiding
volume rendering
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