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Multi-Scale and Kernel-Predicting Convolutional Networks for Monte Carlo Denoising

Published: 20 August 2022 Publication History

Abstract

Monte Carlo rendering has been widely used in many fields, such as movies, which pursue the photorealistic rendering effect. Monte Carlo rendering needs high sampling rates to get an accurate rendering effect, but the calculation cost is expensive. To keep costs down, one solution is to reduce the noise of the rendered image at reduced sampling rates. Because the traditional denoising method is based on higher and higher order regression models, it is prone to overfitting noise in the input. The Monte Carlo denoising method based on deep learning shows a certain denoising value. In this paper, we propose a kernel-predicting convolutional network with a multi-scale residual structure. Compared with previous methods, our method can extract features and perform residual learning at different scales, which can further remove low-frequency noise and improve the denoising quality.

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PRIS '22: Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems
July 2022
102 pages
ISBN:9781450396080
DOI:10.1145/3549179
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2022

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Author Tags

  1. Convolutional Network
  2. Monte Carlo Denoising
  3. Monte Carlo rendering
  4. multi-scale

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