Adaptive Incident Radiance Field Sampling and Reconstruction Using Deep Reinforcement Learning

Citations Over TimeTop 10% of 2020 papers

Abstract

Serious noise affects the rendering of global illumination using Monte Carlo (MC) path tracing when insufficient samples are used. The two common 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 reconstruction 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 competitive performance in biased applications, including filtering and irradiance caching.

Related Papers

• → Light transport simulation with vertex connection and merging(2012)186 cited
• Monte Carlo Methods in Global Illumination: Photo-realistic Rendering with Randomization(2008)
• → A progressive multi-pass method for global illumination(1991)42 cited
• → Unbiased Variance Reduction for Global Illumination(1994)25 cited
• → Adaptive Sampling Based on GH-Distance for Realistic Image Synthesis(2014)