BASED ON IMPROVED POINTNET AUTOMATIC CLASSIFICATION METHOD OF GROTTO TEMPLE STATUES
As a carrier of history, culture, religion and art, grotto temples are an important part of China's splendid cultural heritage. Among them, there are many grotto statues with similar shapes and scattered on the grotto temples, which have high artistic value. It is of great significance to class...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-06-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-annals.copernicus.org/articles/X-M-1-2023/87/2023/isprs-annals-X-M-1-2023-87-2023.pdf |
| _version_ | 1827918146685632512 |
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| author | Q. Fu Q. Fu M. Hou M. Hou W. Hua |
| author_facet | Q. Fu Q. Fu M. Hou M. Hou W. Hua |
| author_sort | Q. Fu |
| collection | DOAJ |
| description | As a carrier of history, culture, religion and art, grotto temples are an important part of China's splendid cultural heritage. Among them, there are many grotto statues with similar shapes and scattered on the grotto temples, which have high artistic value. It is of great significance to classify them.The existing classification methods of grotto temple statues are mainly based on traditional manual classification and machine learning classification, which often consumes a lot of labor costs and time costs. To solve these problems, this paper improves the PointNet network and applies it to the classification of statues in grottoes, which greatly improves the automation of the classification of statues. And the point cloud data set of the grotto temple is made for experiment. The results show that the overall accuracy of the method in this paper reaches 89.73%, the average intersection and combination ratio reaches 68.9%, and the accuracy is increased by 5.47% and 4.3% respectively compared with the random forest classification method. It is of great significance to the value research, status assessment and virtual restoration of the subsequent grotto temples. |
| first_indexed | 2024-03-13T03:38:37Z |
| format | Article |
| id | doaj.art-91113077cbcd4f5892a01047c32d9e3d |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-03-13T03:38:37Z |
| publishDate | 2023-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-91113077cbcd4f5892a01047c32d9e3d2023-06-23T13:14:07ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502023-06-01X-M-1-2023879210.5194/isprs-annals-X-M-1-2023-87-2023BASED ON IMPROVED POINTNET AUTOMATIC CLASSIFICATION METHOD OF GROTTO TEMPLE STATUESQ. Fu0Q. Fu1M. Hou2M. Hou3W. Hua4School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, ChinaBeijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, ChinaBeijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring, ChinaCollege of Geosciences and Surveying Engineering, China University of Mining and Technology, ChinaAs a carrier of history, culture, religion and art, grotto temples are an important part of China's splendid cultural heritage. Among them, there are many grotto statues with similar shapes and scattered on the grotto temples, which have high artistic value. It is of great significance to classify them.The existing classification methods of grotto temple statues are mainly based on traditional manual classification and machine learning classification, which often consumes a lot of labor costs and time costs. To solve these problems, this paper improves the PointNet network and applies it to the classification of statues in grottoes, which greatly improves the automation of the classification of statues. And the point cloud data set of the grotto temple is made for experiment. The results show that the overall accuracy of the method in this paper reaches 89.73%, the average intersection and combination ratio reaches 68.9%, and the accuracy is increased by 5.47% and 4.3% respectively compared with the random forest classification method. It is of great significance to the value research, status assessment and virtual restoration of the subsequent grotto temples.https://isprs-annals.copernicus.org/articles/X-M-1-2023/87/2023/isprs-annals-X-M-1-2023-87-2023.pdf |
| spellingShingle | Q. Fu Q. Fu M. Hou M. Hou W. Hua BASED ON IMPROVED POINTNET AUTOMATIC CLASSIFICATION METHOD OF GROTTO TEMPLE STATUES ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | BASED ON IMPROVED POINTNET AUTOMATIC CLASSIFICATION METHOD OF GROTTO TEMPLE STATUES |
| title_full | BASED ON IMPROVED POINTNET AUTOMATIC CLASSIFICATION METHOD OF GROTTO TEMPLE STATUES |
| title_fullStr | BASED ON IMPROVED POINTNET AUTOMATIC CLASSIFICATION METHOD OF GROTTO TEMPLE STATUES |
| title_full_unstemmed | BASED ON IMPROVED POINTNET AUTOMATIC CLASSIFICATION METHOD OF GROTTO TEMPLE STATUES |
| title_short | BASED ON IMPROVED POINTNET AUTOMATIC CLASSIFICATION METHOD OF GROTTO TEMPLE STATUES |
| title_sort | based on improved pointnet automatic classification method of grotto temple statues |
| url | https://isprs-annals.copernicus.org/articles/X-M-1-2023/87/2023/isprs-annals-X-M-1-2023-87-2023.pdf |
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