Adversarial Monte Carlo denoising with conditioned auxiliary feature modulation

Chuan Li

Abstract: Along with the rapid improvements in hardware and gradually increasing perceptual demands of users, Monte Carlo path tracing is becoming more popular in movie production and video games due to its generality and unbiased nature [Keller et al. 2015; Zwicker et al. 2015]. However, its high estimator variance and low convergence rate motivate researchers to investigate efficient denoising approaches at reduced sample rates with the help of inexpensive by-products (eg, feature buffers). In the past few years, regressionbased kernel filtering approaches [Bitterli et al. 2016; Moon et al. 2014] and learning-based methods [Bako et al. 2017; Chaitanya et al. 2017; Kalantari et al. 2015; Vogels et al. 2018] have achieved great success. In particular, the deep learning based methods have achieved more plausible denoising results, since they effectively leverage convolutional neural networks to break the limitation of only utilizing information from pixel sets in specific images. However, based on our practice of employing the state-of-the-art methods, we found that nearly all of them rely on handcrafted optimization objectives like MSE or MAPE loss which do not necessarily ensure perceptually plausible results. Fig. 1 shows some typical cases where recent works [Bako et al. 2017; Chaitanya et al. 2017] have struggled to handle extremely noisy regions as in high-frequency area thus led to over-smoothed output with approximately correct colors. Our primary focus is to reconstruct the visually convincing global illumination as previous approaches while recovering high-frequency details as much as possible.