Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey
With the rapid development of sensor technologies and the widespread use of laser scanning equipment, point clouds, as the main data form and an important information carrier for 3D scene analysis and understanding, play an essential role in the realization of national strategic needs, such as traff...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/17/3642 |
| _version_ | 1797582682441383936 |
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| author | Rui Zhang Yichao Wu Wei Jin Xiaoman Meng |
| author_facet | Rui Zhang Yichao Wu Wei Jin Xiaoman Meng |
| author_sort | Rui Zhang |
| collection | DOAJ |
| description | With the rapid development of sensor technologies and the widespread use of laser scanning equipment, point clouds, as the main data form and an important information carrier for 3D scene analysis and understanding, play an essential role in the realization of national strategic needs, such as traffic scene perception, natural resource management, and forest biomass carbon stock estimation. As an important research direction in 3D computer vision, point cloud semantic segmentation has attracted more and more researchers’ attention. In this paper, we systematically outline the main research problems and related research methods in point cloud semantic segmentation and summarize the mainstream public datasets and common performance evaluation metrics. Point cloud semantic segmentation methods are classified into rule-based methods and point-based methods according to the representation of the input data. On this basis, the core ideas of each type of segmentation method are introduced, the representative and innovative algorithms of each type of method are elaborated, and the experimental results on the datasets are compared and analyzed. Finally, some promising research directions and potential tendencies are proposed. |
| first_indexed | 2024-03-10T23:24:53Z |
| format | Article |
| id | doaj.art-a0096c417bbb47ca89944a229fe23872 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T23:24:53Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a0096c417bbb47ca89944a229fe238722023-11-19T08:02:02ZengMDPI AGElectronics2079-92922023-08-011217364210.3390/electronics12173642Deep-Learning-Based Point Cloud Semantic Segmentation: A SurveyRui Zhang0Yichao Wu1Wei Jin2Xiaoman Meng3School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, ChinaSchool of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, ChinaSchool of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, ChinaSchool of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, ChinaWith the rapid development of sensor technologies and the widespread use of laser scanning equipment, point clouds, as the main data form and an important information carrier for 3D scene analysis and understanding, play an essential role in the realization of national strategic needs, such as traffic scene perception, natural resource management, and forest biomass carbon stock estimation. As an important research direction in 3D computer vision, point cloud semantic segmentation has attracted more and more researchers’ attention. In this paper, we systematically outline the main research problems and related research methods in point cloud semantic segmentation and summarize the mainstream public datasets and common performance evaluation metrics. Point cloud semantic segmentation methods are classified into rule-based methods and point-based methods according to the representation of the input data. On this basis, the core ideas of each type of segmentation method are introduced, the representative and innovative algorithms of each type of method are elaborated, and the experimental results on the datasets are compared and analyzed. Finally, some promising research directions and potential tendencies are proposed.https://www.mdpi.com/2079-9292/12/17/3642deep learningpoint cloud semantic segmentationconvolutional neural networkfeature representation learningcomputer vision |
| spellingShingle | Rui Zhang Yichao Wu Wei Jin Xiaoman Meng Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey Electronics deep learning point cloud semantic segmentation convolutional neural network feature representation learning computer vision |
| title | Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey |
| title_full | Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey |
| title_fullStr | Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey |
| title_full_unstemmed | Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey |
| title_short | Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey |
| title_sort | deep learning based point cloud semantic segmentation a survey |
| topic | deep learning point cloud semantic segmentation convolutional neural network feature representation learning computer vision |
| url | https://www.mdpi.com/2079-9292/12/17/3642 |
| work_keys_str_mv | AT ruizhang deeplearningbasedpointcloudsemanticsegmentationasurvey AT yichaowu deeplearningbasedpointcloudsemanticsegmentationasurvey AT weijin deeplearningbasedpointcloudsemanticsegmentationasurvey AT xiaomanmeng deeplearningbasedpointcloudsemanticsegmentationasurvey |