Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation

Accurate semantic segmentation of unstructured 3D point clouds requires large amount of annotated training data for deep learning. However, there is currently no free specialized software available that can efficiently annotate large 3D point clouds. We fill this gap by introducing PC-Annotate - a p...

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Autores principales:	Muhammad Ibrahim, Naveed Akhtar, Michael Wise, Ajmal Mian
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	3D point cloud point cloud dataset annotation tool semantic segmentation LiDAR Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/eeb33091a49249d7a9b4542ec567b750
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id	oai:doaj.org-article:eeb33091a49249d7a9b4542ec567b750
record_format	dspace
spelling	oai:doaj.org-article:eeb33091a49249d7a9b4542ec567b7502021-11-09T00:00:26ZAnnotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation2169-353610.1109/ACCESS.2021.3062547https://doaj.org/article/eeb33091a49249d7a9b4542ec567b7502021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9363898/https://doaj.org/toc/2169-3536Accurate semantic segmentation of unstructured 3D point clouds requires large amount of annotated training data for deep learning. However, there is currently no free specialized software available that can efficiently annotate large 3D point clouds. We fill this gap by introducing PC-Annotate - a public annotation tool for 3D point cloud research. The proposed tool not only enables systematic annotation with a variety of fundamental volumetric shapes, but also provides useful functionalities of point cloud registration and the generation of volumetric samples that can be readily consumed by contemporary deep learning point cloud models. We also introduce a large outdoor public dataset for 3D semantic segmentation. The proposed dataset, PC-Urban is collected in a civic setup with Ouster LiDAR and labeled with PC-Annotate. It has over 4.3 billion points covering 66K frames and 25 annotated classes. Finally, we provide baseline semantic segmentation results on PC-Urban for popular recent techniques.Muhammad IbrahimNaveed AkhtarMichael WiseAjmal MianIEEEarticle3D point cloudpoint cloud datasetannotation toolsemantic segmentationLiDARElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 35984-35996 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	3D point cloud point cloud dataset annotation tool semantic segmentation LiDAR Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	3D point cloud point cloud dataset annotation tool semantic segmentation LiDAR Electrical engineering. Electronics. Nuclear engineering TK1-9971 Muhammad Ibrahim Naveed Akhtar Michael Wise Ajmal Mian Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation
description	Accurate semantic segmentation of unstructured 3D point clouds requires large amount of annotated training data for deep learning. However, there is currently no free specialized software available that can efficiently annotate large 3D point clouds. We fill this gap by introducing PC-Annotate - a public annotation tool for 3D point cloud research. The proposed tool not only enables systematic annotation with a variety of fundamental volumetric shapes, but also provides useful functionalities of point cloud registration and the generation of volumetric samples that can be readily consumed by contemporary deep learning point cloud models. We also introduce a large outdoor public dataset for 3D semantic segmentation. The proposed dataset, PC-Urban is collected in a civic setup with Ouster LiDAR and labeled with PC-Annotate. It has over 4.3 billion points covering 66K frames and 25 annotated classes. Finally, we provide baseline semantic segmentation results on PC-Urban for popular recent techniques.
format	article
author	Muhammad Ibrahim Naveed Akhtar Michael Wise Ajmal Mian
author_facet	Muhammad Ibrahim Naveed Akhtar Michael Wise Ajmal Mian
author_sort	Muhammad Ibrahim
title	Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation
title_short	Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation
title_full	Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation
title_fullStr	Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation
title_full_unstemmed	Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation
title_sort	annotation tool and urban dataset for 3d point cloud semantic segmentation
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/eeb33091a49249d7a9b4542ec567b750
work_keys_str_mv	AT muhammadibrahim annotationtoolandurbandatasetfor3dpointcloudsemanticsegmentation AT naveedakhtar annotationtoolandurbandatasetfor3dpointcloudsemanticsegmentation AT michaelwise annotationtoolandurbandatasetfor3dpointcloudsemanticsegmentation AT ajmalmian annotationtoolandurbandatasetfor3dpointcloudsemanticsegmentation
_version_	1718441437156081664

Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation

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