@article{Wu2024, 
author = {Wenshi Wu and Beibei Wang and Miloš Hašan and Lei Zhang and Zhong Jin and Ling-Qi Yan},
title = {Efficient participating media rendering with differentiable regularization},
year = {2024},
journal = {Computational Visual Media},
volume = {10},
number = {5},
pages = {937-948},
keywords = {participating media, differentiable regularization, differentiable rendering, volumetric path tracing, temporal denoising},
url = {https://www.sciopen.com/article/10.1007/s41095-023-0372-2},
doi = {10.1007/s41095-023-0372-2},
abstract = {Highly scattering media, such as milk, skin, and clouds, are common in the real world. Rendering participating media is challenging, especially for high-order scattering dominant media, because the light may undergo a large number of scattering events before leaving the surface. Monte Carlo-based methods typically require a long time to produce noise-free results. Based on the observation that low-albedo media contain less noise than high-albedo media, we propose reducing the variance of the rendered results using differentiable regularization. We first render an image with low-albedo participating media together with the gradient with respect to the albedo, and then predict the final rendered image with a low-albedo image and gradient image via a novel prediction function. To achieve high quality, we also consider the gradients of neighboring frames to provide a noise-free gradient image. Ultimately, our method can produce results with much less overall error than equal-time path tracing methods.}
}