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UASOL, a large-scale high-resolution outdoor stereo dataset
In this paper, we propose a new dataset for outdoor depth estimation from single and stereo RGB images. The dataset was acquired from the point of view of a pedestrian. Currently, the most novel approaches take advantage of deep learning-based techniques, which have proven to outperform traditional...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6715739/ https://www.ncbi.nlm.nih.gov/pubmed/31467361 http://dx.doi.org/10.1038/s41597-019-0168-5 |
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author | Bauer, Zuria Gomez-Donoso, Francisco Cruz, Edmanuel Orts-Escolano, Sergio Cazorla, Miguel |
author_facet | Bauer, Zuria Gomez-Donoso, Francisco Cruz, Edmanuel Orts-Escolano, Sergio Cazorla, Miguel |
author_sort | Bauer, Zuria |
collection | PubMed |
description | In this paper, we propose a new dataset for outdoor depth estimation from single and stereo RGB images. The dataset was acquired from the point of view of a pedestrian. Currently, the most novel approaches take advantage of deep learning-based techniques, which have proven to outperform traditional state-of-the-art computer vision methods. Nonetheless, these methods require large amounts of reliable ground-truth data. Despite there already existing several datasets that could be used for depth estimation, almost none of them are outdoor-oriented from an egocentric point of view. Our dataset introduces a large number of high-definition pairs of color frames and corresponding depth maps from a human perspective. In addition, the proposed dataset also features human interaction and great variability of data, as shown in this work. |
format | Online Article Text |
id | pubmed-6715739 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-67157392019-09-03 UASOL, a large-scale high-resolution outdoor stereo dataset Bauer, Zuria Gomez-Donoso, Francisco Cruz, Edmanuel Orts-Escolano, Sergio Cazorla, Miguel Sci Data Data Descriptor In this paper, we propose a new dataset for outdoor depth estimation from single and stereo RGB images. The dataset was acquired from the point of view of a pedestrian. Currently, the most novel approaches take advantage of deep learning-based techniques, which have proven to outperform traditional state-of-the-art computer vision methods. Nonetheless, these methods require large amounts of reliable ground-truth data. Despite there already existing several datasets that could be used for depth estimation, almost none of them are outdoor-oriented from an egocentric point of view. Our dataset introduces a large number of high-definition pairs of color frames and corresponding depth maps from a human perspective. In addition, the proposed dataset also features human interaction and great variability of data, as shown in this work. Nature Publishing Group UK 2019-08-29 /pmc/articles/PMC6715739/ /pubmed/31467361 http://dx.doi.org/10.1038/s41597-019-0168-5 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Bauer, Zuria Gomez-Donoso, Francisco Cruz, Edmanuel Orts-Escolano, Sergio Cazorla, Miguel UASOL, a large-scale high-resolution outdoor stereo dataset |
title | UASOL, a large-scale high-resolution outdoor stereo dataset |
title_full | UASOL, a large-scale high-resolution outdoor stereo dataset |
title_fullStr | UASOL, a large-scale high-resolution outdoor stereo dataset |
title_full_unstemmed | UASOL, a large-scale high-resolution outdoor stereo dataset |
title_short | UASOL, a large-scale high-resolution outdoor stereo dataset |
title_sort | uasol, a large-scale high-resolution outdoor stereo dataset |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6715739/ https://www.ncbi.nlm.nih.gov/pubmed/31467361 http://dx.doi.org/10.1038/s41597-019-0168-5 |
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