Analysis of the Application of Deep Learning in Model Reconstruction of Ancient Buildings
With the rapid development of interactive 3D graphics technology, as well as the growing demand for virtual reality, digital urbanization and digital cultural heritage protection and time-consuming and inefficient traditional artificial building modeling methods have been far from meeting the rapid...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2022/4273937 |
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| _version_ | 1832549742692794368 |
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| author | Zhihong Wang Hao Xiong |
| author_facet | Zhihong Wang Hao Xiong |
| author_sort | Zhihong Wang |
| collection | DOAJ |
| description | With the rapid development of interactive 3D graphics technology, as well as the growing demand for virtual reality, digital urbanization and digital cultural heritage protection and time-consuming and inefficient traditional artificial building modeling methods have been far from meeting the rapid and intelligent needs of the application market and automatic. Architectural modeling methods have been paid more and more attention. Architectural modeling is an application-oriented comprehensive research field. According to different application scenarios, its research methods cover many technical fields and disciplines. This paper introduces a method of modeling ancient buildings using depth image estimation, spherical projection mapping, 3D adversarial generation network, and other techniques. The characteristics of architectural modeling methods are discussed from different disciplinary and technical perspectives. Second, the three major schools of architectural modeling technology, mainly the process modeling method, image modeling method, and point cloud modeling method, as well as the inverse process modeling method, which has attracted much attention and challenges in recent years, are summarized in detail. Then, the problem of building modeling is discussed. The problems and challenges of building modeling technology are analyzed, and the future development trend is predicted. |
| format | Article |
| id | doaj-art-b97e6e7566b64b11b6f7561d173291b6 |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-b97e6e7566b64b11b6f7561d173291b62025-02-03T06:08:42ZengWileyAdvances in Multimedia1687-56992022-01-01202210.1155/2022/4273937Analysis of the Application of Deep Learning in Model Reconstruction of Ancient BuildingsZhihong Wang0Hao Xiong1Hubei Polytechnic UniversityHubei Polytechnic UniversityWith the rapid development of interactive 3D graphics technology, as well as the growing demand for virtual reality, digital urbanization and digital cultural heritage protection and time-consuming and inefficient traditional artificial building modeling methods have been far from meeting the rapid and intelligent needs of the application market and automatic. Architectural modeling methods have been paid more and more attention. Architectural modeling is an application-oriented comprehensive research field. According to different application scenarios, its research methods cover many technical fields and disciplines. This paper introduces a method of modeling ancient buildings using depth image estimation, spherical projection mapping, 3D adversarial generation network, and other techniques. The characteristics of architectural modeling methods are discussed from different disciplinary and technical perspectives. Second, the three major schools of architectural modeling technology, mainly the process modeling method, image modeling method, and point cloud modeling method, as well as the inverse process modeling method, which has attracted much attention and challenges in recent years, are summarized in detail. Then, the problem of building modeling is discussed. The problems and challenges of building modeling technology are analyzed, and the future development trend is predicted.http://dx.doi.org/10.1155/2022/4273937 |
| spellingShingle | Zhihong Wang Hao Xiong Analysis of the Application of Deep Learning in Model Reconstruction of Ancient Buildings Advances in Multimedia |
| title | Analysis of the Application of Deep Learning in Model Reconstruction of Ancient Buildings |
| title_full | Analysis of the Application of Deep Learning in Model Reconstruction of Ancient Buildings |
| title_fullStr | Analysis of the Application of Deep Learning in Model Reconstruction of Ancient Buildings |
| title_full_unstemmed | Analysis of the Application of Deep Learning in Model Reconstruction of Ancient Buildings |
| title_short | Analysis of the Application of Deep Learning in Model Reconstruction of Ancient Buildings |
| title_sort | analysis of the application of deep learning in model reconstruction of ancient buildings |
| url | http://dx.doi.org/10.1155/2022/4273937 |
| work_keys_str_mv | AT zhihongwang analysisoftheapplicationofdeeplearninginmodelreconstructionofancientbuildings AT haoxiong analysisoftheapplicationofdeeplearninginmodelreconstructionofancientbuildings |