EEI-NET: EDGE-ENHANCED INTERPOLATION NETWORK FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDING POINT CLOUDS
In recent years, the conservation research of historical buildings and cultural relics has received a lot of attention from the state and the people, which not only provides a deeper understanding of their historical value and cultural significance, but also promotes the expansion of conservation re...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-12-01
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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-1-W1-2023/239/2023/isprs-annals-X-1-W1-2023-239-2023.pdf |
| _version_ | 1797403715728048128 |
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| author | Y. Xue R. Zhang R. Zhang R. Zhang J. Wang J. Wang J. Zhao J. Zhao J. Zhao L. Pang |
| author_facet | Y. Xue R. Zhang R. Zhang R. Zhang J. Wang J. Wang J. Zhao J. Zhao J. Zhao L. Pang |
| author_sort | Y. Xue |
| collection | DOAJ |
| description | In recent years, the conservation research of historical buildings and cultural relics has received a lot of attention from the state and the people, which not only provides a deeper understanding of their historical value and cultural significance, but also promotes the expansion of conservation research to the three-dimensional level. In this context, the semantic segmentation of historical building components is particularly important, which can provide basic support for various historical building applications, such as research and study of historical buildings, repair and protection, and 3D fine reconstruction, etc. However, most of the current methods for semantic segmentation of point clouds of historical buildings suffer from the problems of not being able to fully exploit the local neighborhood information of point clouds and poor edge segmentation. Therefore, we propose a new deep learning semantic segmentation-based approach, which we call EEI-Net. It is an end-to-end deep neural network in which we designed an edge enhancement interpolation (EEI) module and an edge interaction classifier (EIC). The edge enhancement interpolation module performs edge enhancement interpolation by fusing multi-layer features between the encoder and decoder. The edge interaction classifier enables the interaction of edge information through information transfer between individual nodes. EEI-Net incorporates contextual features and better preserves and enhances the edge information of the point cloud. We conduct experiments on the constructed historical architecture dataset, and the results show that the proposed EEI-Net has better performance. |
| first_indexed | 2024-03-09T02:43:25Z |
| format | Article |
| id | doaj.art-89a1bcb3db354bddbfabac0ef63083d4 |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-03-09T02:43:25Z |
| publishDate | 2023-12-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-89a1bcb3db354bddbfabac0ef63083d42023-12-05T23:56:07ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502023-12-01X-1-W1-202323924510.5194/isprs-annals-X-1-W1-2023-239-2023EEI-NET: EDGE-ENHANCED INTERPOLATION NETWORK FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDING POINT CLOUDSY. Xue0R. Zhang1R. Zhang2R. Zhang3J. Wang4J. Wang5J. Zhao6J. Zhao7J. Zhao8L. Pang9School of Geomantic and Urban Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Geomantic and Urban Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaEngineering Research Center of Representative Building and Architectural Heritage Database, Ministry of Education, Beijing 102616, ChinaBeijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring, Beijing 102616, ChinaSchool of Geomantic and Urban Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaInstitute of Science and Technology Development, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Geomantic and Urban Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaEngineering Research Center of Representative Building and Architectural Heritage Database, Ministry of Education, Beijing 102616, ChinaBeijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring, Beijing 102616, ChinaSchool of Geomantic and Urban Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaIn recent years, the conservation research of historical buildings and cultural relics has received a lot of attention from the state and the people, which not only provides a deeper understanding of their historical value and cultural significance, but also promotes the expansion of conservation research to the three-dimensional level. In this context, the semantic segmentation of historical building components is particularly important, which can provide basic support for various historical building applications, such as research and study of historical buildings, repair and protection, and 3D fine reconstruction, etc. However, most of the current methods for semantic segmentation of point clouds of historical buildings suffer from the problems of not being able to fully exploit the local neighborhood information of point clouds and poor edge segmentation. Therefore, we propose a new deep learning semantic segmentation-based approach, which we call EEI-Net. It is an end-to-end deep neural network in which we designed an edge enhancement interpolation (EEI) module and an edge interaction classifier (EIC). The edge enhancement interpolation module performs edge enhancement interpolation by fusing multi-layer features between the encoder and decoder. The edge interaction classifier enables the interaction of edge information through information transfer between individual nodes. EEI-Net incorporates contextual features and better preserves and enhances the edge information of the point cloud. We conduct experiments on the constructed historical architecture dataset, and the results show that the proposed EEI-Net has better performance.https://isprs-annals.copernicus.org/articles/X-1-W1-2023/239/2023/isprs-annals-X-1-W1-2023-239-2023.pdf |
| spellingShingle | Y. Xue R. Zhang R. Zhang R. Zhang J. Wang J. Wang J. Zhao J. Zhao J. Zhao L. Pang EEI-NET: EDGE-ENHANCED INTERPOLATION NETWORK FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDING POINT CLOUDS ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | EEI-NET: EDGE-ENHANCED INTERPOLATION NETWORK FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDING POINT CLOUDS |
| title_full | EEI-NET: EDGE-ENHANCED INTERPOLATION NETWORK FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDING POINT CLOUDS |
| title_fullStr | EEI-NET: EDGE-ENHANCED INTERPOLATION NETWORK FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDING POINT CLOUDS |
| title_full_unstemmed | EEI-NET: EDGE-ENHANCED INTERPOLATION NETWORK FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDING POINT CLOUDS |
| title_short | EEI-NET: EDGE-ENHANCED INTERPOLATION NETWORK FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDING POINT CLOUDS |
| title_sort | eei net edge enhanced interpolation network for semantic segmentation of historical building point clouds |
| url | https://isprs-annals.copernicus.org/articles/X-1-W1-2023/239/2023/isprs-annals-X-1-W1-2023-239-2023.pdf |
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