High-Quality Real-Time Rendering Using Subpixel Sampling Reconstruction

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 necessitate a substantial time expenditure when rendering at high resolutions due to the physically-based sampling and network inference time burdens. 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.

Authors

• Boyu Zhang SenseTime Research
• Hongliang Yuan Xiaomi Cooperation Tencent AI Lab

Keywords

• CV: Applications
• CV: Computational Photography, Image & Video Synthesis
• CV: Low Level & Physics-based Vision