Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review
In the cultural heritage field, point clouds, as important raw data of geomatics, are not only three-dimensional (3D) spatial presentations of 3D objects but they also have the potential to gradually advance towards an intelligent data structure with scene understanding, autonomous cognition, and a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/3/548 |
| _version_ | 1797623407010906112 |
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| author | Su Yang Miaole Hou Songnian Li |
| author_facet | Su Yang Miaole Hou Songnian Li |
| author_sort | Su Yang |
| collection | DOAJ |
| description | In the cultural heritage field, point clouds, as important raw data of geomatics, are not only three-dimensional (3D) spatial presentations of 3D objects but they also have the potential to gradually advance towards an intelligent data structure with scene understanding, autonomous cognition, and a decision-making ability. The approach of point cloud semantic segmentation as a preliminary stage can help to realize this advancement. With the demand for semantic comprehensibility of point cloud data and the widespread application of machine learning and deep learning approaches in point cloud semantic segmentation, there is a need for a comprehensive literature review covering the topics from the point cloud data acquisition to semantic segmentation algorithms with application strategies in cultural heritage. This paper first reviews the current trends of acquiring point cloud data of cultural heritage from a single platform with multiple sensors and multi-platform collaborative data fusion. Then, the point cloud semantic segmentation algorithms are discussed with their advantages, disadvantages, and specific applications in the cultural heritage field. These algorithms include region growing, model fitting, unsupervised clustering, supervised machine learning, and deep learning. In addition, we summarized the public benchmark point cloud datasets related to cultural heritage. Finally, the problems and constructive development trends of 3D point cloud semantic segmentation in the cultural heritage field are presented. |
| first_indexed | 2024-03-11T09:28:29Z |
| format | Article |
| id | doaj.art-8fc71e55cfa04bdfb6bce9db58ca5758 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T09:28:29Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8fc71e55cfa04bdfb6bce9db58ca57582023-11-16T17:50:47ZengMDPI AGRemote Sensing2072-42922023-01-0115354810.3390/rs15030548Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive ReviewSu Yang0Miaole Hou1Songnian Li2College of Geosciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, ChinaSchool of Geomatics and Urban Information, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaDepartment of Civil Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, CanadaIn the cultural heritage field, point clouds, as important raw data of geomatics, are not only three-dimensional (3D) spatial presentations of 3D objects but they also have the potential to gradually advance towards an intelligent data structure with scene understanding, autonomous cognition, and a decision-making ability. The approach of point cloud semantic segmentation as a preliminary stage can help to realize this advancement. With the demand for semantic comprehensibility of point cloud data and the widespread application of machine learning and deep learning approaches in point cloud semantic segmentation, there is a need for a comprehensive literature review covering the topics from the point cloud data acquisition to semantic segmentation algorithms with application strategies in cultural heritage. This paper first reviews the current trends of acquiring point cloud data of cultural heritage from a single platform with multiple sensors and multi-platform collaborative data fusion. Then, the point cloud semantic segmentation algorithms are discussed with their advantages, disadvantages, and specific applications in the cultural heritage field. These algorithms include region growing, model fitting, unsupervised clustering, supervised machine learning, and deep learning. In addition, we summarized the public benchmark point cloud datasets related to cultural heritage. Finally, the problems and constructive development trends of 3D point cloud semantic segmentation in the cultural heritage field are presented.https://www.mdpi.com/2072-4292/15/3/548point cloudsemantic segmentationclassificationcultural heritagemachine learningdeep learning |
| spellingShingle | Su Yang Miaole Hou Songnian Li Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review Remote Sensing point cloud semantic segmentation classification cultural heritage machine learning deep learning |
| title | Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review |
| title_full | Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review |
| title_fullStr | Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review |
| title_full_unstemmed | Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review |
| title_short | Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review |
| title_sort | three dimensional point cloud semantic segmentation for cultural heritage a comprehensive review |
| topic | point cloud semantic segmentation classification cultural heritage machine learning deep learning |
| url | https://www.mdpi.com/2072-4292/15/3/548 |
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