dmariaa70/ets2-dataset

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The declared licence (mit) is classified as permissive.

Declared licence: mit, from the tagEvidence: README.md at 4741aaf
Licence files: No licence file at the top level of the repositoryEvidence: file list at 4741aaf
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Citation text from the card

If you use this dataset in your research, please cite the following paper:

```bibtex
@article{Mara-Arribas2023,
   abstract = {In this work, we present a new dataset for monocular depth estimation created by extracting images, dense depth maps, and odometer data from a realistic video game simulation, Euro Truck Simulator 2 TM. The dataset is used to train state-of-the-art depth estimation models in both supervised and unsupervised ways, which are evaluated against real-world sequences. Our results demonstrate that models trained exclusively with synthetic data achieve satisfactory performance in the real domain. The quantitative evaluation brings light to possible causes of domain gap in monocular depth estimation. Specifically, we discuss the effects of coarse-grained ground-truth depth maps in contrast to the fine-grained depth estimation. The dataset and code for data extraction and experiments are released open-source.},
   author = {David María-Arribas and Alfredo Cuesta-Infante and Juan J. Pantrigo},
   doi = {10.1007/978-3-031-36616-1_30},
   isbn = {978-3-031-36616-1},
   issn = {16113349},
   journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
   keywords = {depth estimation,domain gap,simulation,synthetic dataset,video games},
   month = {6},
   pages = {375-386},
   publisher = {Springer Nature},
   title = {The ETS2 Dataset, Synthetic Data from Video Games for Monocular

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dataset_licence({ "id": "dmariaa70/ets2-dataset" })
signal: "permissive"
stated_licence: "mit"
sources: 0