Deep Learning on 3D Semantic Segmentation: A Detailed Review
In this paper, an exhaustive review and comprehensive analysis of recent and former deep learning methods in 3D semantic segmentation (3DSS) is presented. In the related literature, the taxonomy scheme used for the classification of 3DSS deep learning methods is ambiguous. Based on the taxonomy sche...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/298 |
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| author | Thodoris Betsas Andreas Georgopoulos Anastasios Doulamis Pierre Grussenmeyer |
| author_facet | Thodoris Betsas Andreas Georgopoulos Anastasios Doulamis Pierre Grussenmeyer |
| author_sort | Thodoris Betsas |
| collection | DOAJ |
| description | In this paper, an exhaustive review and comprehensive analysis of recent and former deep learning methods in 3D semantic segmentation (3DSS) is presented. In the related literature, the taxonomy scheme used for the classification of 3DSS deep learning methods is ambiguous. Based on the taxonomy schemes of nine existing review papers, a new taxonomy scheme for 3DSS deep learning methods is proposed, aiming to standardize it and improve the comparability and clarity across related studies. Furthermore, an extensive overview of the available 3DSS indoor and outdoor datasets is provided along with their links. The core part of this review is the detailed presentation of recent and former 3DSS deep learning methods and their classification using the proposed taxonomy scheme along with their GitHub repositories. Additionally, a brief but informative analysis of the evaluation metrics and loss functions used in 3DSS is included. Finally, a fruitful discussion of the examined 3DSS methods and datasets is presented to foster new research directions and applications in the field of 3DSS. In addition to this review, a GitHub repository is provided, including an initial classification of over 400 3DSS methods, using the proposed taxonomy scheme. |
| format | Article |
| id | doaj-art-f4d530b24abb41ceb758ca44397652ff |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-f4d530b24abb41ceb758ca44397652ff2025-01-24T13:48:02ZengMDPI AGRemote Sensing2072-42922025-01-0117229810.3390/rs17020298Deep Learning on 3D Semantic Segmentation: A Detailed ReviewThodoris Betsas0Andreas Georgopoulos1Anastasios Doulamis2Pierre Grussenmeyer3Laboratory of Photogrammetry, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15772 Athens, GreeceLaboratory of Photogrammetry, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15772 Athens, GreeceLaboratory of Photogrammetry, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15772 Athens, GreeceICube Laboratory UMR 7357, CNRS, INSA Strasbourg, Université de Strasbourg, 67084 Strasbourg, FranceIn this paper, an exhaustive review and comprehensive analysis of recent and former deep learning methods in 3D semantic segmentation (3DSS) is presented. In the related literature, the taxonomy scheme used for the classification of 3DSS deep learning methods is ambiguous. Based on the taxonomy schemes of nine existing review papers, a new taxonomy scheme for 3DSS deep learning methods is proposed, aiming to standardize it and improve the comparability and clarity across related studies. Furthermore, an extensive overview of the available 3DSS indoor and outdoor datasets is provided along with their links. The core part of this review is the detailed presentation of recent and former 3DSS deep learning methods and their classification using the proposed taxonomy scheme along with their GitHub repositories. Additionally, a brief but informative analysis of the evaluation metrics and loss functions used in 3DSS is included. Finally, a fruitful discussion of the examined 3DSS methods and datasets is presented to foster new research directions and applications in the field of 3DSS. In addition to this review, a GitHub repository is provided, including an initial classification of over 400 3DSS methods, using the proposed taxonomy scheme.https://www.mdpi.com/2072-4292/17/2/2983D semantic segmentationpoint cloudsdeep learningreview |
| spellingShingle | Thodoris Betsas Andreas Georgopoulos Anastasios Doulamis Pierre Grussenmeyer Deep Learning on 3D Semantic Segmentation: A Detailed Review Remote Sensing 3D semantic segmentation point clouds deep learning review |
| title | Deep Learning on 3D Semantic Segmentation: A Detailed Review |
| title_full | Deep Learning on 3D Semantic Segmentation: A Detailed Review |
| title_fullStr | Deep Learning on 3D Semantic Segmentation: A Detailed Review |
| title_full_unstemmed | Deep Learning on 3D Semantic Segmentation: A Detailed Review |
| title_short | Deep Learning on 3D Semantic Segmentation: A Detailed Review |
| title_sort | deep learning on 3d semantic segmentation a detailed review |
| topic | 3D semantic segmentation point clouds deep learning review |
| url | https://www.mdpi.com/2072-4292/17/2/298 |
| work_keys_str_mv | AT thodorisbetsas deeplearningon3dsemanticsegmentationadetailedreview AT andreasgeorgopoulos deeplearningon3dsemanticsegmentationadetailedreview AT anastasiosdoulamis deeplearningon3dsemanticsegmentationadetailedreview AT pierregrussenmeyer deeplearningon3dsemanticsegmentationadetailedreview |