High-Quality Real-Time Rendering Using Subpixel Sampling Reconstruction
Abstract
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.
BibTeX
@article{Zhang_Yuan_2024, title={High-Quality Real-Time Rendering Using Subpixel Sampling Reconstruction}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/28527}, DOI={10.1609/aaai.v38i7.28527}, abstractNote={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.}, number={7}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Zhang, Boyu and Yuan, Hongliang}, year={2024}, month={Mar.}, pages={7006-7014} }