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- W4313532671 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." @default.
- W4313532671 created "2023-01-06" @default.
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- W4313532671 date "2023-01-03" @default.
- W4313532671 modified "2023-10-17" @default.
- W4313532671 title "High-Quality Real-Time Rendering Using Subpixel Sampling Reconstruction" @default.
- W4313532671 doi "https://doi.org/10.48550/arxiv.2301.01036" @default.
- W4313532671 hasPublicationYear "2023" @default.
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