Denoising Monte Carlo Renders with Diffusion Models
This work addresses the challenge of reducing compute costs in rendering by enabling faster renders with fewer rays per pixel, though it is incremental as it builds on existing learned methods.
The paper tackles the problem of Monte Carlo noise in physically-based renderings by using a diffusion model to denoise low-fidelity renders, achieving competitive performance with state-of-the-art methods across various sampling rates.
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.