Denoising Monte Carlo Renders with Diffusion Models

Physically-based rendering lies at the core of a number of creative applications in gaming, cinema, and design. Unfortunately, these synthetic images often contain unwanted 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. 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.”