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.

翻译：本文研究利用超分辨率技术减少需要渲染的像素数量，从而加速蒙特卡洛渲染算法。尽管超分辨率技术已取得重大进展，但其本质上仍是一个病态问题，无法恢复渲染结果中的高频细节。为解决该问题，我们利用高分辨率辅助特征引导低分辨率渲染结果的超分辨率重建。这些高分辨率辅助特征可由渲染引擎快速生成，同时提供有价值的高频细节信息以辅助超分辨率过程。为此，我们提出跨模态Transformer网络，该网络包含辅助特征分支和低分辨率渲染分支，两个分支被设计用于融合高分辨率辅助特征与对应低分辨率渲染结果。此外，我们设计了残差密集连接Swin Transformer组，通过学习提取具有代表性的特征以实现高质量超分辨率重建。实验表明，在生成高质量渲染结果方面，我们提出的辅助特征引导超分辨率方法优于现有超分辨率方法和蒙特卡洛去噪方法。