Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping

Paris-CARLA-3D is a dataset of several dense colored point clouds of outdoor environments built by a mobile LiDAR and camera system. The data are composed of two sets with synthetic data from the open source CARLA simulator (700 million points) and real data acquired in the city of Paris (60 million...

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Autores principales:	Jean-Emmanuel Deschaud, David Duque, Jean Pierre Richa, Santiago Velasco-Forero, Beatriz Marcotegui, François Goulette
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	dataset LiDAR mobile mapping laser scanning 3D mapping synthetic Science Q
Acceso en línea:	https://doaj.org/article/e766eab895d74dd6a5d660a9ae6a42e6
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id	oai:doaj.org-article:e766eab895d74dd6a5d660a9ae6a42e6
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spelling	oai:doaj.org-article:e766eab895d74dd6a5d660a9ae6a42e62021-11-25T18:55:40ZParis-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping10.3390/rs132247132072-4292https://doaj.org/article/e766eab895d74dd6a5d660a9ae6a42e62021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4713https://doaj.org/toc/2072-4292Paris-CARLA-3D is a dataset of several dense colored point clouds of outdoor environments built by a mobile LiDAR and camera system. The data are composed of two sets with synthetic data from the open source CARLA simulator (700 million points) and real data acquired in the city of Paris (60 million points), hence the name Paris-CARLA-3D. One of the advantages of this dataset is to have simulated the same LiDAR and camera platform in the open source CARLA simulator as the one used to produce the real data. In addition, manual annotation of the classes using the semantic tags of CARLA was performed on the real data, allowing the testing of transfer methods from the synthetic to the real data. The objective of this dataset is to provide a challenging dataset to evaluate and improve methods on difficult vision tasks for the 3D mapping of outdoor environments: semantic segmentation, instance segmentation, and scene completion. For each task, we describe the evaluation protocol as well as the experiments carried out to establish a baseline.Jean-Emmanuel DeschaudDavid DuqueJean Pierre RichaSantiago Velasco-ForeroBeatriz MarcoteguiFrançois GouletteMDPI AGarticledatasetLiDARmobile mappinglaser scanning3D mappingsyntheticScienceQENRemote Sensing, Vol 13, Iss 4713, p 4713 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	dataset LiDAR mobile mapping laser scanning 3D mapping synthetic Science Q
spellingShingle	dataset LiDAR mobile mapping laser scanning 3D mapping synthetic Science Q Jean-Emmanuel Deschaud David Duque Jean Pierre Richa Santiago Velasco-Forero Beatriz Marcotegui François Goulette Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping
description	Paris-CARLA-3D is a dataset of several dense colored point clouds of outdoor environments built by a mobile LiDAR and camera system. The data are composed of two sets with synthetic data from the open source CARLA simulator (700 million points) and real data acquired in the city of Paris (60 million points), hence the name Paris-CARLA-3D. One of the advantages of this dataset is to have simulated the same LiDAR and camera platform in the open source CARLA simulator as the one used to produce the real data. In addition, manual annotation of the classes using the semantic tags of CARLA was performed on the real data, allowing the testing of transfer methods from the synthetic to the real data. The objective of this dataset is to provide a challenging dataset to evaluate and improve methods on difficult vision tasks for the 3D mapping of outdoor environments: semantic segmentation, instance segmentation, and scene completion. For each task, we describe the evaluation protocol as well as the experiments carried out to establish a baseline.
format	article
author	Jean-Emmanuel Deschaud David Duque Jean Pierre Richa Santiago Velasco-Forero Beatriz Marcotegui François Goulette
author_facet	Jean-Emmanuel Deschaud David Duque Jean Pierre Richa Santiago Velasco-Forero Beatriz Marcotegui François Goulette
author_sort	Jean-Emmanuel Deschaud
title	Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping
title_short	Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping
title_full	Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping
title_fullStr	Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping
title_full_unstemmed	Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping
title_sort	paris-carla-3d: a real and synthetic outdoor point cloud dataset for challenging tasks in 3d mapping
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e766eab895d74dd6a5d660a9ae6a42e6
work_keys_str_mv	AT jeanemmanueldeschaud pariscarla3darealandsyntheticoutdoorpointclouddatasetforchallengingtasksin3dmapping AT davidduque pariscarla3darealandsyntheticoutdoorpointclouddatasetforchallengingtasksin3dmapping AT jeanpierrericha pariscarla3darealandsyntheticoutdoorpointclouddatasetforchallengingtasksin3dmapping AT santiagovelascoforero pariscarla3darealandsyntheticoutdoorpointclouddatasetforchallengingtasksin3dmapping AT beatrizmarcotegui pariscarla3darealandsyntheticoutdoorpointclouddatasetforchallengingtasksin3dmapping AT francoisgoulette pariscarla3darealandsyntheticoutdoorpointclouddatasetforchallengingtasksin3dmapping
_version_	1718410561088126976

Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping

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