Ganet: graph attention based Terracotta Warriors point cloud completion network
Abstract Point cloud completion technology is used to address incomplete three-dimensional point cloud data, predicting and reconstructing the original shape and details to achieve virtual restoration. While existing learning-based methods have made significant progress in point cloud completion, th...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-11-01
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| Series: | Heritage Science |
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| Online Access: | https://doi.org/10.1186/s40494-024-01487-9 |
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| _version_ | 1846158327488184320 |
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| author | Jian Gao Yuhe Zhang Gaoxue Shiqin Pengbo Zhou Yue Wen Guohua Geng |
| author_facet | Jian Gao Yuhe Zhang Gaoxue Shiqin Pengbo Zhou Yue Wen Guohua Geng |
| author_sort | Jian Gao |
| collection | DOAJ |
| description | Abstract Point cloud completion technology is used to address incomplete three-dimensional point cloud data, predicting and reconstructing the original shape and details to achieve virtual restoration. While existing learning-based methods have made significant progress in point cloud completion, they still face challenges when dealing with noise and invisible data. To address these issues, this paper proposes a multi-layer upsampling network based on a graph attention mechanism, called GANet. GANet consists of three main components: (1) feature extraction; (2) seed point generation; (3) State Space Model-based Point Cloud Upsampling Layer. GANet demonstrates exceptional robustness in handling noise and invisible data. To validate the effectiveness of GANet, we applied it to Terracotta Warrior data. The Terracotta Warriors, as important cultural heritage, present a challenging test case due to damage and missing parts caused by prolonged burial and environmental factors. We trained and tested GANet on both the PCN dataset and Terracotta Warrior data, comparing it with several recent learning-based methods. Experimental results show that GANet can effectively reconstruct missing or damaged parts of 3D point clouds, providing more detailed and structurally accurate completion results. These completion models not only validate GANet’s effectiveness but also offer valuable references for cultural heritage restoration work. |
| format | Article |
| id | doaj-art-8c9c0a83b1224faaa9e7d49a2e074d3a |
| institution | Kabale University |
| issn | 2050-7445 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Heritage Science |
| spelling | doaj-art-8c9c0a83b1224faaa9e7d49a2e074d3a2024-11-24T12:38:20ZengSpringerOpenHeritage Science2050-74452024-11-0112111410.1186/s40494-024-01487-9Ganet: graph attention based Terracotta Warriors point cloud completion networkJian Gao0Yuhe Zhang1Gaoxue Shiqin2Pengbo Zhou3Yue Wen4Guohua Geng5School of Information Science and Technology, Northwest UniversitySchool of Information Science and Technology, Northwest UniversitySchool of Information Science and Technology, Northwest UniversitySchool of Arts and Communication, Beijing Normal UniversitySchool of law, Wuhan UniversitySchool of Information Science and Technology, Northwest UniversityAbstract Point cloud completion technology is used to address incomplete three-dimensional point cloud data, predicting and reconstructing the original shape and details to achieve virtual restoration. While existing learning-based methods have made significant progress in point cloud completion, they still face challenges when dealing with noise and invisible data. To address these issues, this paper proposes a multi-layer upsampling network based on a graph attention mechanism, called GANet. GANet consists of three main components: (1) feature extraction; (2) seed point generation; (3) State Space Model-based Point Cloud Upsampling Layer. GANet demonstrates exceptional robustness in handling noise and invisible data. To validate the effectiveness of GANet, we applied it to Terracotta Warrior data. The Terracotta Warriors, as important cultural heritage, present a challenging test case due to damage and missing parts caused by prolonged burial and environmental factors. We trained and tested GANet on both the PCN dataset and Terracotta Warrior data, comparing it with several recent learning-based methods. Experimental results show that GANet can effectively reconstruct missing or damaged parts of 3D point clouds, providing more detailed and structurally accurate completion results. These completion models not only validate GANet’s effectiveness but also offer valuable references for cultural heritage restoration work.https://doi.org/10.1186/s40494-024-01487-9Terracotta WarriorsVirtual repairPoint Cloud CompletionDeep learning |
| spellingShingle | Jian Gao Yuhe Zhang Gaoxue Shiqin Pengbo Zhou Yue Wen Guohua Geng Ganet: graph attention based Terracotta Warriors point cloud completion network Heritage Science Terracotta Warriors Virtual repair Point Cloud Completion Deep learning |
| title | Ganet: graph attention based Terracotta Warriors point cloud completion network |
| title_full | Ganet: graph attention based Terracotta Warriors point cloud completion network |
| title_fullStr | Ganet: graph attention based Terracotta Warriors point cloud completion network |
| title_full_unstemmed | Ganet: graph attention based Terracotta Warriors point cloud completion network |
| title_short | Ganet: graph attention based Terracotta Warriors point cloud completion network |
| title_sort | ganet graph attention based terracotta warriors point cloud completion network |
| topic | Terracotta Warriors Virtual repair Point Cloud Completion Deep learning |
| url | https://doi.org/10.1186/s40494-024-01487-9 |
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