Path Tracing Projects

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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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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Project

A Practical Path Guiding Method for Participating Media

Hong Deng¹, Beibei Wang¹, Rui Wang², and Nicolas Holzschuc³

¹ Nanjing University of Science and Technolog, China
²State Key Laboratory of CAD & CG, Zhejiang University, China
³Univ. Grenoble-Alpes, Inria, , France

Computational Visual Media, 2020, Vol. 6, No. 1, 37-51. (Spotlight Paper)

Abstract:

Rendering translucent materials is costly: light transport algorithms need to simulate a large number of scattering events inside the material before reaching convergence. The cost is especially high for materials with a large albedo or a small mean-free-path, where higher-order scattering effects dominate. In simple terms, the paths get lost in the medium. Path guiding has been proposed for surface rendering to make convergence faster by guiding the sampling process. In this paper, we introduce a path guiding solution for translucent materials. We learn an adaptive approximate representation of the radiance distribution in the volume and use it to sample the scattering direction, combining it with phase function sampling by resampled importance sampling. The proposed method significantly improves the performance of light transport simulation in participating media, especially for small lights and media with refractive boundaries. Our method can handle any homogeneous participating medium, with high or low scattering, with high or low absorption, and from isotropic to highly anisotropic.

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