A compact representation for multiple scattering in participating media using neural networks

Many materials, such as milk or wax, exhibit scattering effects; incoming light enters the material and is scattered inside, giving a translucent aspect. These effects are computationally intensive as they require simulating a large number of events. Full computations are expensive, even with accelerating methods such as Virtual Ray Lights. The dipole approximation [Jensen et al. 2001] is fast, but a strong approximation. Precomputing the material response for multiple scattering [Moon et al. 2007; Wang and Holzschuch 2017] integrates well with existing rendering algorithms, allowing separate computation for single- and double- scattering, and fast computation for multiple scattering. Their main issue is efficient storage for the precomputed multiple scattering data.