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A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds

The computer vision, graphics, and machine learning research groups have given a significant amount of focus to 3D object recognition (segmentation, detection, and classification). Deep learning approaches have lately emerged as the preferred method for 3D segmentation problems as a result of their...

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Detalles Bibliográficos
Autores principales: Vinodkumar, Prasoon Kumar, Karabulut, Dogus, Avots, Egils, Ozcinar, Cagri, Anbarjafari, Gholamreza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137403/
https://www.ncbi.nlm.nih.gov/pubmed/37190423
http://dx.doi.org/10.3390/e25040635
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author Vinodkumar, Prasoon Kumar
Karabulut, Dogus
Avots, Egils
Ozcinar, Cagri
Anbarjafari, Gholamreza
author_facet Vinodkumar, Prasoon Kumar
Karabulut, Dogus
Avots, Egils
Ozcinar, Cagri
Anbarjafari, Gholamreza
author_sort Vinodkumar, Prasoon Kumar
collection PubMed
description The computer vision, graphics, and machine learning research groups have given a significant amount of focus to 3D object recognition (segmentation, detection, and classification). Deep learning approaches have lately emerged as the preferred method for 3D segmentation problems as a result of their outstanding performance in 2D computer vision. As a result, many innovative approaches have been proposed and validated on multiple benchmark datasets. This study offers an in-depth assessment of the latest developments in deep learning-based 3D object recognition. We discuss the most well-known 3D object recognition models, along with evaluations of their distinctive qualities.
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spelling pubmed-101374032023-04-28 A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds Vinodkumar, Prasoon Kumar Karabulut, Dogus Avots, Egils Ozcinar, Cagri Anbarjafari, Gholamreza Entropy (Basel) Review The computer vision, graphics, and machine learning research groups have given a significant amount of focus to 3D object recognition (segmentation, detection, and classification). Deep learning approaches have lately emerged as the preferred method for 3D segmentation problems as a result of their outstanding performance in 2D computer vision. As a result, many innovative approaches have been proposed and validated on multiple benchmark datasets. This study offers an in-depth assessment of the latest developments in deep learning-based 3D object recognition. We discuss the most well-known 3D object recognition models, along with evaluations of their distinctive qualities. MDPI 2023-04-10 /pmc/articles/PMC10137403/ /pubmed/37190423 http://dx.doi.org/10.3390/e25040635 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Vinodkumar, Prasoon Kumar
Karabulut, Dogus
Avots, Egils
Ozcinar, Cagri
Anbarjafari, Gholamreza
A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds
title A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds
title_full A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds
title_fullStr A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds
title_full_unstemmed A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds
title_short A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds
title_sort survey on deep learning based segmentation, detection and classification for 3d point clouds
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137403/
https://www.ncbi.nlm.nih.gov/pubmed/37190423
http://dx.doi.org/10.3390/e25040635
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