Implementation of Autoencoder based Neural Network for Realtime Ray Tracing
HakHyun Lee,Dept. Computer Engineering, SeoKyeong Univ., 124, Seogyeong-ro, Seongbuk-gu, Seoul, Republic of Korea
Chelwon Jo,Dept. Computer Engineering, SeoKyeong Univ., 124, Seogyeong-ro, Seongbuk-gu, Seoul, Republic of Korea
Kwang-Yeob Lee,Dept. Computer Engineering, SeoKyeong Univ., 124, Seogyeong-ro, Seongbuk-gu, Seoul, Republic of Korea
This paper proposes a denoising neural network for real-time ray tracing. The ray tracing method is applied in graphics to increase reality and in particular, Monte Carlo Rendering is most effective. However, ray tracing that applies Monte Carlo Rendering has a steep rise in the amount of calculations with the increase of the number of rays. Therefore, in order to solve this problem, various methods are being proposed to reduce the number of rays and to decrease the occurring noise. In this paper, an autoencoder-based neural network that can effectively remove noise while using a small number of rays was implemented. An autoencoder that uses a 1×1 convolution in creating the last feature map was proposed to significantly lower the amount of calculation. The proposed structure can handle 8196 spp ray-tracing image in 20 seconds at 64 spp.
Ray tracing, Denoising, Monte carlo rendering, Neural network, Autoencoder
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© 2020 HakHyun Lee et al. Published by Global Vision Press. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CCBY4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.