Shapenet Illuminants - dataset from "Zero-Shot Day-Night Domain Adaptation with a Physics Prior"

doi: 10.4121/15141273.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/15141273
Datacite citation style:
Attila Lengyel (2021): Shapenet Illuminants - dataset from "Zero-Shot Day-Night Domain Adaptation with a Physics Prior". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/15141273.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
Delft University of Technology logo
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licence
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Shapenet Illuminants is the synthetic classification dataset used in the ICCV '21 publication "Zero-Shot Day-Night Domain Adaptation with a Physics Prior". The images have been rendered from the ShapeNet dataset using the Mitsuba rendering engine. See the readme for more information on using the dataset.

ArXiv: https://arxiv.org/abs/2108.05137
Code: https://github.com/Attila94/CIConv

If you find this dataset useful, please cite:
@article{lengyel2021zeroshot,
title={Zero-Shot Domain Adaptation with a Physics Prior},
author={Attila Lengyel and Sourav Garg and Michael Milford and Jan C. van Gemert},
year={2021},
eprint={2108.05137},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
history
  • 2021-08-12 first online, published, posted
publisher
4TU.ResearchData
format
image/png
funding
  • Tabula Inscripta: Prior knowledge for deep learning (grant code VI.Vidi.192.100) [more info...] Dutch Research Council
organizations
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems;
QUT Centre for Robotics

DATA

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