Deep radiance caching: Convolutional autoencoders deeper in ray tracing

Rendering realistic images with global illumination is a computationally demanding task and often requires dedicated hardware for feasible runtime. Recent research uses Deep Neural Networks to predict indirect lighting on image level, but such methods are commonly limited to diffuse materials and require training on each scene. We present Deep Radiance Caching (DRC), an efficient variant of Radiance Caching utilizing Convolutional Autoencoders for rendering global illumination. DRC employs a denoising neural network with Radiance Caching to support a wide range of material types, without the requirement of offline pre-computation or training for each scene. This offers high performance CPU rendering for maximum accessibility. Our method has been evaluated on interior scenes, and is able to produce high-quality images within 180 s on a single CPU. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

Rendering realistic images with global illumination is a computationally demanding task. Recent research utilizes Deep Neural Networks to predict indirect lighting on image level, commonly limited to diffuse materials and requiring training on each scene. Deep Radiance Caching (DRC) is presented as an efficient variant of Radiance Caching utilizing Convolutional Autoencoders for rendering global illumination, supporting a wide range of material types without the need for offline pre-computation or training for each scene.