A Review: Point Cloud-Based 3D Human Joints Estimation
Joint estimation of the human body is suitable for many fields such as human–computer interaction, autonomous driving, video analysis and virtual reality. Although many depth-based researches have been classified and generalized in previous review or survey papers, the point cloud-based pose estimat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/5/1684 |
| _version_ | 1797416526382366720 |
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| author | Tianxu Xu Dong An Yuetong Jia Yang Yue |
| author_facet | Tianxu Xu Dong An Yuetong Jia Yang Yue |
| author_sort | Tianxu Xu |
| collection | DOAJ |
| description | Joint estimation of the human body is suitable for many fields such as human–computer interaction, autonomous driving, video analysis and virtual reality. Although many depth-based researches have been classified and generalized in previous review or survey papers, the point cloud-based pose estimation of human body is still difficult due to the disorder and rotation invariance of the point cloud. In this review, we summarize the recent development on the point cloud-based pose estimation of the human body. The existing works are divided into three categories based on their working principles, including template-based method, feature-based method and machine learning-based method. Especially, the significant works are highlighted with a detailed introduction to analyze their characteristics and limitations. The widely used datasets in the field are summarized, and quantitative comparisons are provided for the representative methods. Moreover, this review helps further understand the pertinent applications in many frontier research directions. Finally, we conclude the challenges involved and problems to be solved in future researches. |
| first_indexed | 2024-03-09T06:05:29Z |
| format | Article |
| id | doaj.art-8067c17ef0414d3398f289d704957b58 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T06:05:29Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8067c17ef0414d3398f289d704957b582023-12-03T12:04:21ZengMDPI AGSensors1424-82202021-03-01215168410.3390/s21051684A Review: Point Cloud-Based 3D Human Joints EstimationTianxu Xu0Dong An1Yuetong Jia2Yang Yue3Institute of Modern Optics, Nankai University, Tianjin 300350, ChinaInstitute of Modern Optics, Nankai University, Tianjin 300350, ChinaInstitute of Modern Optics, Nankai University, Tianjin 300350, ChinaInstitute of Modern Optics, Nankai University, Tianjin 300350, ChinaJoint estimation of the human body is suitable for many fields such as human–computer interaction, autonomous driving, video analysis and virtual reality. Although many depth-based researches have been classified and generalized in previous review or survey papers, the point cloud-based pose estimation of human body is still difficult due to the disorder and rotation invariance of the point cloud. In this review, we summarize the recent development on the point cloud-based pose estimation of the human body. The existing works are divided into three categories based on their working principles, including template-based method, feature-based method and machine learning-based method. Especially, the significant works are highlighted with a detailed introduction to analyze their characteristics and limitations. The widely used datasets in the field are summarized, and quantitative comparisons are provided for the representative methods. Moreover, this review helps further understand the pertinent applications in many frontier research directions. Finally, we conclude the challenges involved and problems to be solved in future researches.https://www.mdpi.com/1424-8220/21/5/1684point cloudjoint estimationskeleton extractiondepth sensorskeleton trackingcomputer vision |
| spellingShingle | Tianxu Xu Dong An Yuetong Jia Yang Yue A Review: Point Cloud-Based 3D Human Joints Estimation Sensors point cloud joint estimation skeleton extraction depth sensor skeleton tracking computer vision |
| title | A Review: Point Cloud-Based 3D Human Joints Estimation |
| title_full | A Review: Point Cloud-Based 3D Human Joints Estimation |
| title_fullStr | A Review: Point Cloud-Based 3D Human Joints Estimation |
| title_full_unstemmed | A Review: Point Cloud-Based 3D Human Joints Estimation |
| title_short | A Review: Point Cloud-Based 3D Human Joints Estimation |
| title_sort | review point cloud based 3d human joints estimation |
| topic | point cloud joint estimation skeleton extraction depth sensor skeleton tracking computer vision |
| url | https://www.mdpi.com/1424-8220/21/5/1684 |
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