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

DOI:10.4121/15141273.v1
The DOI displayed 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

Usage statistics

1312
views
2047
downloads

Licence

CC BY-NC 4.0
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/2108.05137Code: 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

Files (3)