Denoising Production Volumetric Rendering.

Denoising is an integral part of production rendering pipelines that use Monte-Carlo (MC) path tracing. Machine learning based denoisers have been proven to effectively remove the residual noise and produce a clean image. However, denoising volumetric rendering remains a problem due to the lack of useful features and large-scale volume datasets. We have seen issues such as over-blurring and temporal flickering in the denoised sequence. In this work, we modify the production renderer to generate potential features that might improve the denoising quality, and then run a state-of-the-art feature selection algorithm to detect the best combination. We collect thousands of unique volumetric scenes from our recent films to create a large dataset for training. Our evaluation shows a good amount of quality gain compared to the version currently in use.