Auxiliary Features-Guided Super Resolution for Monte Carlo Rendering
Qiqi Hou Feng Liu
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.