One Shot Radiance: Global Illumination Using Convolutional Autoencoders
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Rendering realistic images with Global Illumination (GI) is a computationally demanding task and often requires dedicated hardware for feasible runtime. Recent projects have used Generative Adversarial Networks (GAN) to predict indirect lighting on an image level, but are limited to diffuse materials and require training on each scene. We present One-Shot Radiance (OSR), a novel machine learning technique for rendering Global Illumination using Convolutional Autoencoders. We combine a modern denoising Neural Network with Radiance Caching to offer high performance CPU GI rendering while supporting a wide range of material types, without the requirement of offline pre-computation or training for each scene. OSR has been evaluated on interior scenes, and is able to produce high-quality images within 180 seconds on a single CPU.
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