High-Quality Real-Time Rendering Using Subpixel Sampling Reconstruction

• URL: http://arxiv.org/abs/2301.01036v2
• Date: Sun, 25 Jun 2023 16:19:23 GMT
• Title: High-Quality Real-Time Rendering Using Subpixel Sampling Reconstruction
• Authors: Boyu Zhang, Hongliang Yuan, Mingyan Zhu, Ligang Liu, Jue Wang
• Abstract summary: We propose a novel Monte Carlo sampling strategy to accelerate the sampling process and a corresponding denoiser, subpixel sampling reconstruction (SSR) Our method significantly outperforms previous approaches in denoising quality and reduces overall time costs, enabling real-time rendering capabilities at 2K resolution.
• Score: 20.431360489828975
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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