Imperial College London
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DR BERNHARD KAINZ

Faculty of Engineering, Department of Computing

Reader in Medical Image Computing
 
 
 
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Contact

 

+44 (0)20 7594 8349b.kainz Website CV

 
 
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Location

 

372Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Giulio:2021:10.1016/j.cag.2020.09.007,
author = {Giulio, J and Kainz, B},
doi = {10.1016/j.cag.2020.09.007},
journal = {Computers and Graphics (UK)},
pages = {22--31},
title = {Deep radiance caching: Convolutional autoencoders deeper in ray tracing},
url = {http://dx.doi.org/10.1016/j.cag.2020.09.007},
volume = {94},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Giulio,J
AU  - Kainz,B
DO  - 10.1016/j.cag.2020.09.007
EP  - 31
PY  - 2021///
SN  - 0097-8493
SP  - 22
TI  - Deep radiance caching: Convolutional autoencoders deeper in ray tracing
T2  - Computers and Graphics (UK)
UR  - http://dx.doi.org/10.1016/j.cag.2020.09.007
UR  - http://hdl.handle.net/10044/1/83969
VL  - 94
ER  -
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