Path Tracing Projects

Members: Rui Wang, Yuchi Huo, Hualin Xu, Shihao Li, Xuchen Wei, Hujun Bao

Adaptive Incident Radiance Field Sampling and Reconstruction Using Deep Reinforcement Learning

Yuchi Huo, Rui Wang, Ruzhang Zheng, Hualin Xu, Hujun Bao Sung-Eui Yong

KAIST,
State Key Lab of CAD&CG, Zhejiang University


ACM Transactions on Graphics (TOG), 39(1), Article 6, will be presented in ACM SIGGRAPH 2020

Abstract:

Serious noise affects the rendering of global illumination using Monte Carlo (MC) path tracing when insufficient samples are used. The two com- mon solutions to this problem are filtering noisy inputs to generate smooth but biased results and sampling the MC integrand with a carefully crafted probability distribution function (PDF) to produce unbiased results. Both solutions benefit from an efficient incident radiance field sampling and recon- struction algorithm. This study proposes a method for training quality and reconstruction networks (Q- and R-networks, respectively) with a massive offline dataset for the adaptive sampling and reconstruction of first-bounce incident radiance fields. The convolutional neural network (CNN)-based R-network reconstructs the incident radiance field in a 4D space, whereas the deep reinforcement learning (DRL)-based Q-network predicts and guides the adaptive sampling process. The approach is verified by comparing it with state-of-the-art unbiased path guiding methods and filtering methods. Results demonstrate improvements for unbiased path guiding and competi- tive performance in biased applications, including filtering and irradiance caching.

ACM Transactions on Graphics (TOG) 39(1) cover image:

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Paper
Supplemental Document

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,
   2State Key Laboratory of CAD & CG, Zhejiang University, China
   3BNRist, Department of Computer Science and Technology, Tsinghua University, China
   4University of Bath, UK
   5Lambda Labs Inc, USA


ACM Transactions on Graphics (TOG), 38(6), 12 pages, ACM SIGGRAPH ASIA 2019

Abstract:

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.

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