Highly scattering media,such as milk,skin,and clouds,are common in the real world.Rendering participating media is challenging,especially for highorder scattering dominant media,because the light may undergo a large n...Highly scattering media,such as milk,skin,and clouds,are common in the real world.Rendering participating media is challenging,especially for highorder 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 eror than equal-time path tracing methods.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.62172220。
文摘Highly scattering media,such as milk,skin,and clouds,are common in the real world.Rendering participating media is challenging,especially for highorder 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 eror than equal-time path tracing methods.
