Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Bauer, Zuria, Gomez-Donoso, Francisco, Cruz, Edmanuel, Orts-Escolano, Sergio, Cazorla, Miguel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
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
_version_ 1783447272301789184
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
work_keys_str_mv AT bauerzuria uasolalargescalehighresolutionoutdoorstereodataset
AT gomezdonosofrancisco uasolalargescalehighresolutionoutdoorstereodataset
AT cruzedmanuel uasolalargescalehighresolutionoutdoorstereodataset
AT ortsescolanosergio uasolalargescalehighresolutionoutdoorstereodataset
AT cazorlamiguel uasolalargescalehighresolutionoutdoorstereodataset