Adversarial Monte Carlo Denoising with Conditioned Auxiliary Feature Modulation

Bing Xu, Junfei Zhang, Rui Wang, Kun Xu, Yongliang Yang, Chuan Li, Rui Tang

Research output: Contribution to journalArticlepeer-review

59 Citations (SciVal)
109 Downloads (Pure)


Denoising Monte Carlo rendering with a very low sample rate remains a major challenge in the photo-realistic rendering research. Many previous works, including regression-based and learning-based methods, have been explored to achieve better rendering quality with less computational cost. However, most of these methods rely on handcrafted optimization objectives, which lead to artifacts such as blurs and unfaithful details. In this paper, we present an adversarial approach for denoising Monte Carlo rendering. Our key insight is that generative adversarial networks can help denoiser networks to produce more realistic high-frequency details and global illumination by learning the distribution from a set of high-quality Monte Carlo path tracing images. We also adapt a novel feature modulation method to utilize auxiliary features better, including normal, albedo and depth. Compared to previous state-of-the-art methods, our approach produces a better reconstruction of the Monte Carlo integral from a few samples, performs more robustly at different sample rates, and takes only a second for megapixel images.
Original languageEnglish
Article number224
JournalACM Transactions on Graphics
Issue number6
Publication statusPublished - 30 Nov 2019


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