Survey of Point Cloud Semantic Segmentation Based on Deep Learning
In recent years, the popularity of depth sensors and 3D laserscanners has led to a rapid development of 3D point clouds processing methods. Semantic segmentation of point cloud, as a key step in understanding 3D scenes, has attracted extensive attention of researchers. With the rapid development of...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2021-01-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/CN/abstract/abstract2520.shtml |
| _version_ | 1818718077753229312 |
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| author | JING Zhuangwei, GUAN Haiyan, ZANG Yufu, NI Huan, LI Dilong, YU Yongtao |
| author_facet | JING Zhuangwei, GUAN Haiyan, ZANG Yufu, NI Huan, LI Dilong, YU Yongtao |
| author_sort | JING Zhuangwei, GUAN Haiyan, ZANG Yufu, NI Huan, LI Dilong, YU Yongtao |
| collection | DOAJ |
| description | In recent years, the popularity of depth sensors and 3D laserscanners has led to a rapid development of 3D point clouds processing methods. Semantic segmentation of point cloud, as a key step in understanding 3D scenes, has attracted extensive attention of researchers. With the rapid development of deep learning and its widespread applications in 3D semantic segmentation, the quality of point cloud semantic segmentation has been significantly improved. This paper mainly reviews the deep learning-based point cloud semantic segmentation methods and their research status. This paper categories these deep learning-based methods for point clouds into two groups: indirect and direct semantic segmentation methods. In terms of the characteristics of the algorithm, each of groups is further subdivided. The representative algorithms are analyzed and introduced. This paper summarizes the theories, principles, advantages and disadvantages of each type of method, and systematically explains the contribution of deep learning to the field of semantic segmentation. Moreover, the current mainstream datasets and remote sensing datasets are summarized and the experimental results of some algorithms are compared. Finally, the future development direction of semantic segmentation technology is prospected. |
| first_indexed | 2024-12-17T19:45:19Z |
| format | Article |
| id | doaj.art-df8b75ec28a64302b919731ea93ae18e |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-12-17T19:45:19Z |
| publishDate | 2021-01-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-df8b75ec28a64302b919731ea93ae18e2022-12-21T21:34:53ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182021-01-0115112610.3778/j.issn.1673-9418.2006025Survey of Point Cloud Semantic Segmentation Based on Deep LearningJING Zhuangwei, GUAN Haiyan, ZANG Yufu, NI Huan, LI Dilong, YU Yongtao01. School of Geographical Science, Nanjing University of Information Science & Technology, Nanjing 210044, China 2. School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China 3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China 4. Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai??an, Jiangsu 223003, ChinaIn recent years, the popularity of depth sensors and 3D laserscanners has led to a rapid development of 3D point clouds processing methods. Semantic segmentation of point cloud, as a key step in understanding 3D scenes, has attracted extensive attention of researchers. With the rapid development of deep learning and its widespread applications in 3D semantic segmentation, the quality of point cloud semantic segmentation has been significantly improved. This paper mainly reviews the deep learning-based point cloud semantic segmentation methods and their research status. This paper categories these deep learning-based methods for point clouds into two groups: indirect and direct semantic segmentation methods. In terms of the characteristics of the algorithm, each of groups is further subdivided. The representative algorithms are analyzed and introduced. This paper summarizes the theories, principles, advantages and disadvantages of each type of method, and systematically explains the contribution of deep learning to the field of semantic segmentation. Moreover, the current mainstream datasets and remote sensing datasets are summarized and the experimental results of some algorithms are compared. Finally, the future development direction of semantic segmentation technology is prospected.http://fcst.ceaj.org/CN/abstract/abstract2520.shtmldeep learningsemantic segmentationpoint cloudcomputer vision |
| spellingShingle | JING Zhuangwei, GUAN Haiyan, ZANG Yufu, NI Huan, LI Dilong, YU Yongtao Survey of Point Cloud Semantic Segmentation Based on Deep Learning Jisuanji kexue yu tansuo deep learning semantic segmentation point cloud computer vision |
| title | Survey of Point Cloud Semantic Segmentation Based on Deep Learning |
| title_full | Survey of Point Cloud Semantic Segmentation Based on Deep Learning |
| title_fullStr | Survey of Point Cloud Semantic Segmentation Based on Deep Learning |
| title_full_unstemmed | Survey of Point Cloud Semantic Segmentation Based on Deep Learning |
| title_short | Survey of Point Cloud Semantic Segmentation Based on Deep Learning |
| title_sort | survey of point cloud semantic segmentation based on deep learning |
| topic | deep learning semantic segmentation point cloud computer vision |
| url | http://fcst.ceaj.org/CN/abstract/abstract2520.shtml |
| work_keys_str_mv | AT jingzhuangweiguanhaiyanzangyufunihuanlidilongyuyongtao surveyofpointcloudsemanticsegmentationbasedondeeplearning |