Generating high-quality, realistic rendering images for real-time applications generally requires tracing a few samples-per-pixel (spp) and using deep learning-based approaches to denoise the resulting low-spp images. Existing denoising methods have yet to achieve real-time performance at high resolutions due to the physically-based sampling and network inference time costs. In this paper, we propose a novel Monte Carlo sampling strategy to accelerate the sampling process and a corresponding denoiser, subpixel sampling reconstruction (SSR), to obtain high-quality images. Extensive experiments demonstrate that our method significantly outperforms previous approaches in denoising quality and reduces overall time costs, enabling real-time rendering capabilities at 2K resolution.
@article{arxiv.2301.01036,
title = {High-Quality Real-Time Rendering Using Subpixel Sampling Reconstruction},
author = {Boyu Zhang and Hongliang Yuan and Mingyan Zhu and Ligang Liu and Jue Wang},
journal= {arXiv preprint arXiv:2301.01036},
year = {2023}
}