# Robust Average Networks for Monte Carlo Denoising

arXiv: https://arxiv.org/abs/2310.04080 · HTML: https://mlsanity.com/paper/2310.04080/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2023.

## Built on by (most cited first, 1 of 1)

- [Real-Time Neural Hair G-Buffer Anti-Aliasing](https://mlsanity.com/paper/2605.17557/) (2026)

## Abstract (arXiv)

We present a method for converting denoising neural networks from spatial into spatio-temporal ones by modifying the network architecture and loss function. We insert Robust Average blocks at arbitrary depths in the network graph. Each block performs latent space interpolation with trainable weights and works on the sequence of image representations from the preceding spatial components of the network. The temporal connections are kept live during training by forcing the network to predict a denoised frame from subsets of the input sequence. Using temporal coherence for denoising improves image quality and reduces temporal flickering independent of scene or image complexity.