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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10137403 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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