Multiple Cylinder Extraction from Organized Point Clouds

Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most fr...

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Autores principales:	Saed Moradi, Denis Laurendeau, Clement Gosselin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	organized point clouds depth map surface normal estimation cylinder detection cylinder extraction Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/74a43944c8054d7da3b3811731fd3d6d
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spelling	oai:doaj.org-article:74a43944c8054d7da3b3811731fd3d6d2021-11-25T18:58:04ZMultiple Cylinder Extraction from Organized Point Clouds10.3390/s212276301424-8220https://doaj.org/article/74a43944c8054d7da3b3811731fd3d6d2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7630https://doaj.org/toc/1424-8220Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most frequently used GPs in real-world scenes. Therefore, cylinder detection and extraction are of great importance in 3D computer vision. Despite the rapid progress of cylinder detection algorithms, there are still two open problems in this area. First, a robust strategy is needed for the initial sample selection component of the cylinder extraction module. Second, detecting multiple cylinders simultaneously has not yet been investigated in depth. In this paper, a robust solution is provided to address these problems. The proposed solution is divided into three sub-modules. The first sub-module is a fast and accurate normal vector estimation algorithm from raw depth images. With the estimation method, a closed-form solution is provided for computing the normal vector at each point. The second sub-module benefits from the maximally stable extremal regions (MSER) feature detector to simultaneously detect cylinders present in the scene. Finally, the detected cylinders are extracted using the proposed cylinder extraction algorithm. Quantitative and qualitative results show that the proposed algorithm outperforms the baseline algorithms in each of the following areas: normal estimation, cylinder detection, and cylinder extraction.Saed MoradiDenis LaurendeauClement GosselinMDPI AGarticleorganized point cloudsdepth mapsurface normal estimationcylinder detectioncylinder extractionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7630, p 7630 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	organized point clouds depth map surface normal estimation cylinder detection cylinder extraction Chemical technology TP1-1185
spellingShingle	organized point clouds depth map surface normal estimation cylinder detection cylinder extraction Chemical technology TP1-1185 Saed Moradi Denis Laurendeau Clement Gosselin Multiple Cylinder Extraction from Organized Point Clouds
description	Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most frequently used GPs in real-world scenes. Therefore, cylinder detection and extraction are of great importance in 3D computer vision. Despite the rapid progress of cylinder detection algorithms, there are still two open problems in this area. First, a robust strategy is needed for the initial sample selection component of the cylinder extraction module. Second, detecting multiple cylinders simultaneously has not yet been investigated in depth. In this paper, a robust solution is provided to address these problems. The proposed solution is divided into three sub-modules. The first sub-module is a fast and accurate normal vector estimation algorithm from raw depth images. With the estimation method, a closed-form solution is provided for computing the normal vector at each point. The second sub-module benefits from the maximally stable extremal regions (MSER) feature detector to simultaneously detect cylinders present in the scene. Finally, the detected cylinders are extracted using the proposed cylinder extraction algorithm. Quantitative and qualitative results show that the proposed algorithm outperforms the baseline algorithms in each of the following areas: normal estimation, cylinder detection, and cylinder extraction.
format	article
author	Saed Moradi Denis Laurendeau Clement Gosselin
author_facet	Saed Moradi Denis Laurendeau Clement Gosselin
author_sort	Saed Moradi
title	Multiple Cylinder Extraction from Organized Point Clouds
title_short	Multiple Cylinder Extraction from Organized Point Clouds
title_full	Multiple Cylinder Extraction from Organized Point Clouds
title_fullStr	Multiple Cylinder Extraction from Organized Point Clouds
title_full_unstemmed	Multiple Cylinder Extraction from Organized Point Clouds
title_sort	multiple cylinder extraction from organized point clouds
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/74a43944c8054d7da3b3811731fd3d6d
work_keys_str_mv	AT saedmoradi multiplecylinderextractionfromorganizedpointclouds AT denislaurendeau multiplecylinderextractionfromorganizedpointclouds AT clementgosselin multiplecylinderextractionfromorganizedpointclouds
_version_	1718410469239160832

Multiple Cylinder Extraction from Organized Point Clouds

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