Adversarial Monte Carlo Denoising with Conditioned Auxiliary Feature Modulation
BING XU1 JUNFEI ZHANG1 RUI WANG2 KUN XU3 YONG-LIANG YANG4 CHUAN LI5 RUI TANG1
1KooLab, Kujiale, China
3BNRist, Department of Computer Science and Technology, Tsinghua University, China
2State Key Laboratory of CAD & CG, Zhejiang University, China 4University of Bath, UK 5Lambda Labs Inc, USA
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.
Code & Dataset
Use the interactive viewer to inspect the image details of our methods.