Published In

Computer Graphics Forum

Document Type


Publication Date



Monte Carlo method, Dielectric measurements, Electromagnetic waves -- Scattering


This paper investigates super-resolution to reduce the number of pixels to render and thus speed up Monte Carlo rendering algorithms. While great progress has been made to super-resolution technologies, it is essentially an ill-posed problem and cannot recover high-frequency details in renderings. To address this problem, we exploit high-resolution auxiliary features to guide super-resolution of low-resolution renderings. These high-resolution auxiliary features can be quickly rendered by a rendering engine and at the same time provide valuable high-frequency details to assist super-resolution. To this end, we develop a cross-modality transformer network that consists of an auxiliary feature branch and a low-resolution rendering branch. These two branches are designed to fuse high-resolution auxiliary features with the corresponding low-resolution rendering. Furthermore, we design Residual Densely Connected Swin Transformer groups to learn to extract representative features to enable high-quality super-resolution. Our experiments show that our auxiliary features-guided super-resolution method outperforms both super-resolution methods and Monte Carlo denoising methods in producing high-quality renderings.


Copyright (c) 2023 The Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Locate the Document