  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 seconds
on a single CPU.
