Denoising Monte Carlo Renders with Diffusion Models

TL;DR

This paper introduces a diffusion model-based approach to denoise Monte Carlo renderings, effectively reducing noise in low-sample images and outperforming existing methods in quality and realism.

Contribution

The authors develop a diffusion model that denoises Monte Carlo renders, conditioned on render information, achieving state-of-the-art performance and realistic reconstructions.

Findings

Competitive with SOTA across sampling rates

Produces realistic images with straight shadow boundaries and no fireflies

Conditioning improves denoising performance

Abstract

Physically-based renderings contain Monte-Carlo noise, with variance that increases as the number of rays per pixel decreases. This noise, while zero-mean for good modern renderers, can have heavy tails (most notably, for scenes containing specular or refractive objects). Learned methods for restoring low fidelity renders are highly developed, because suppressing render noise means one can save compute and use fast renders with few rays per pixel. We demonstrate that a diffusion model can denoise low fidelity renders successfully. Furthermore, our method can be conditioned on a variety of natural render information, and this conditioning helps performance. Quantitative experiments show that our method is competitive with SOTA across a range of sampling rates. Qualitative examination of the reconstructions suggests that the image prior applied by a diffusion method strongly favors reconstructions that are like real images -- so have straight shadow boundaries, curved specularities and no fireflies.