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.

这篇论文研究了超分辨率，以减少像素数量和加速蒙特卡洛渲染算法。通过利用高分辨率辅助特征来指导低分辨率渲染的超分辨率，我们开发了一个跨模态 Transformer 网络，该网络由一个辅助特征分支和一个低分辨率渲染分支组成，通过融合高分辨率辅助特征和相应的低分辨率渲染来实现。我们还设计了残差密集连接的 Swin Transformer 组来学习提取代表性特征，以实现高质量的超分辨率。实验证明，我们的辅助特征引导的超分辨率方法在生成高质量渲染方面优于超分辨率方法和蒙特卡洛去噪方法。