Soroor Malekmohammadi Faradounbeh, SeongKi Kim, Journal of Information Processing Systems Vol. 17, No. 4, pp. 737-753, Aug. 2021  

https://doi.org/10.3745/JIPS.02.0162
Keywords: Denoising, Filtering, Global Illumination, Monte Carlo Noise, Noise Removal
Fulltext: PDF Full Text PubReader

Abstract

As the demand for high-quality rendering for mixed reality, videogame, and simulation has increased, global illumination has been actively researched. Monte Carlo path tracing can realize global illumination and produce photorealistic scenes that include critical effects such as color bleeding, caustics, multiple light, and shadows. If the sampling rate is insufficient, however, the rendered results have a large amount of noise. The most successful approach to eliminating or reducing Monte Carlo noise uses a feature-based filter. It exploits the scene characteristics such as a position within a world coordinate and a shading normal. In general, the techniques are based on the denoised pixel or sample and are computationally expensive. However, the main challenge for all of them is to find the appropriate weights for every feature while preserving the details of the scene. In this paper, we compare the recent algorithms for removing Monte Carlo noise in terms of their performance and quality. We also describe their advantages and disadvantages. As far as we know, this study is the first in the world to compare the artificial intelligence-based denoising methods for Monte Carlo rendering.

Statistics
Cite this article