Automatic reconstruction of semantic façade model of architectural heritage
Abstract Façade elements, such as windows, doors, balconies, sculptures, and totems, in architectural heritage images with incomplete structures should be automatically reconstructed for applications in 3D analysis, 3D modeling, virtual tourism, city planning, and the protection and reconstruction o...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-11-01
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| Series: | Heritage Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40494-024-01506-9 |
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| _version_ | 1846164983840243712 |
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| author | Jingwei Hou Ji Zhou Yonghong He Bo Hou Jia Li |
| author_facet | Jingwei Hou Ji Zhou Yonghong He Bo Hou Jia Li |
| author_sort | Jingwei Hou |
| collection | DOAJ |
| description | Abstract Façade elements, such as windows, doors, balconies, sculptures, and totems, in architectural heritage images with incomplete structures should be automatically reconstructed for applications in 3D analysis, 3D modeling, virtual tourism, city planning, and the protection and reconstruction of architectural heritage. This study segments façade elements of architectural heritage semantically using YOLOv9. A parameterized expression for the semantic façade model is designed. In addition, the façade layer graph (FLG) and element layer graph (ELG) algorithms are developed based on topological, geometric, and structural constraints to automatically reconstruct the semantic façade model for architectural heritages. The results showed that the average precision (AP) and mean intersection over union (MIoU) achieved using YOLOv9 + FLG-ELG are 86.91% and 85.63%, respectively, on the dataset concerning façade elements of architectural heritages. The AP values obtained from the proposed method are 98.5% on the ECP2011 dataset and 95.3% on the Graz2012 dataset. The YOLOv9 + FLG-ELG method automatically reconstructs regular, irregular, and complex façade layouts with high accuracy and robustness. |
| format | Article |
| id | doaj-art-8da5eb5c7d874c22860add19d5668fab |
| institution | Kabale University |
| issn | 2050-7445 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Heritage Science |
| spelling | doaj-art-8da5eb5c7d874c22860add19d5668fab2024-11-17T12:40:57ZengSpringerOpenHeritage Science2050-74452024-11-0112111410.1186/s40494-024-01506-9Automatic reconstruction of semantic façade model of architectural heritageJingwei Hou0Ji Zhou1Yonghong He2Bo Hou3Jia Li4Hunan Provincial Key Laboratory of Intelligent Protection and Utilization Technology in Masonry Artifacts, Hunan University of Science and EngineeringHunan Provincial Key Laboratory of Intelligent Protection and Utilization Technology in Masonry Artifacts, Hunan University of Science and EngineeringHunan Provincial Key Laboratory of Intelligent Protection and Utilization Technology in Masonry Artifacts, Hunan University of Science and EngineeringCollege of Media, Hunan University of Science and EngineeringSchool of Civil and Environmental Engineering, Hunan University of Science and EngineeringAbstract Façade elements, such as windows, doors, balconies, sculptures, and totems, in architectural heritage images with incomplete structures should be automatically reconstructed for applications in 3D analysis, 3D modeling, virtual tourism, city planning, and the protection and reconstruction of architectural heritage. This study segments façade elements of architectural heritage semantically using YOLOv9. A parameterized expression for the semantic façade model is designed. In addition, the façade layer graph (FLG) and element layer graph (ELG) algorithms are developed based on topological, geometric, and structural constraints to automatically reconstruct the semantic façade model for architectural heritages. The results showed that the average precision (AP) and mean intersection over union (MIoU) achieved using YOLOv9 + FLG-ELG are 86.91% and 85.63%, respectively, on the dataset concerning façade elements of architectural heritages. The AP values obtained from the proposed method are 98.5% on the ECP2011 dataset and 95.3% on the Graz2012 dataset. The YOLOv9 + FLG-ELG method automatically reconstructs regular, irregular, and complex façade layouts with high accuracy and robustness.https://doi.org/10.1186/s40494-024-01506-9Automatic reconstructionSemantic segmentationFaçade elementArchitectural heritage |
| spellingShingle | Jingwei Hou Ji Zhou Yonghong He Bo Hou Jia Li Automatic reconstruction of semantic façade model of architectural heritage Heritage Science Automatic reconstruction Semantic segmentation Façade element Architectural heritage |
| title | Automatic reconstruction of semantic façade model of architectural heritage |
| title_full | Automatic reconstruction of semantic façade model of architectural heritage |
| title_fullStr | Automatic reconstruction of semantic façade model of architectural heritage |
| title_full_unstemmed | Automatic reconstruction of semantic façade model of architectural heritage |
| title_short | Automatic reconstruction of semantic façade model of architectural heritage |
| title_sort | automatic reconstruction of semantic facade model of architectural heritage |
| topic | Automatic reconstruction Semantic segmentation Façade element Architectural heritage |
| url | https://doi.org/10.1186/s40494-024-01506-9 |
| work_keys_str_mv | AT jingweihou automaticreconstructionofsemanticfacademodelofarchitecturalheritage AT jizhou automaticreconstructionofsemanticfacademodelofarchitecturalheritage AT yonghonghe automaticreconstructionofsemanticfacademodelofarchitecturalheritage AT bohou automaticreconstructionofsemanticfacademodelofarchitecturalheritage AT jiali automaticreconstructionofsemanticfacademodelofarchitecturalheritage |