Denoising-Aware Adaptive Sampling for Monte Carlo Ray Tracing

Firmino, Arthur; Frisvad, Jeppe Revall; Jensen, Henrik Wann

doi:10.1145/3588432.3591537

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Deep Learning-based Denoising1

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2024

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Cited by 2 publications

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“…To reduce the overhead of kernel prediction, Fan et al (Fan et al 2021) predict an encoding of the kernel map, followed by a high-efficiency decoder to construct the complete kernel map. (Firmino, Frisvad, and Jensen 2023) designe adaptive sampling for optimizing MC denoising. (Balint et al 2023) employe pyramid filters to recover renderings.…”

Section: Deep Learning-based Denoisingmentioning

confidence: 99%

High-Quality Real-Time Rendering Using Subpixel Sampling Reconstruction

Zhang,

Yuan

2024

AAAI

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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.

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Section: Deep Learning-based Denoisingmentioning

confidence: 99%