Deep Radiance Caching: Convolutional Autoencoders Deeper in Ray Tracing

Giulio Jiang; Bernhard Kainz

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arXiv · 1910.02480

Deep Radiance Caching: Convolutional Autoencoders Deeper in Ray Tracing

Giulio Jiang·Bernhard Kainz

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Abstract

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.

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BibTeX RIS

Giulio Jiang, Bernhard Kainz. 2019-10-06. Deep Radiance Caching: Convolutional Autoencoders Deeper in Ray Tracing. https://arxiv.org/abs/1910.02480

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