  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.
