Intelligent City 3D Modeling Model Based on Multisource Data Point Cloud Algorithm
With the rapid development of smart cities, intelligent navigation, and autonomous driving, how to quickly obtain 3D spatial information of urban buildings and build a high-precision 3D fine model has become a key problem to be solved. As the two-dimensional mapping results have constrained various...
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Published in | Journal of function spaces Vol. 2022; pp. 1 - 10 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
New York
Hindawi
2022
Hindawi Limited Wiley |
Subjects | |
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Abstract | With the rapid development of smart cities, intelligent navigation, and autonomous driving, how to quickly obtain 3D spatial information of urban buildings and build a high-precision 3D fine model has become a key problem to be solved. As the two-dimensional mapping results have constrained various needs in people’s social life, coupled with the concept of digital city and advocacy, making three-dimensional, virtualization and actualization become the common pursuit of people’s goals. However, the original point cloud obtained is always incomplete due to reasons such as occlusion during acquisition and data density decreasing with distance, resulting in extracted boundaries that are often incomplete as well. In this paper, based on the study of current mainstream 3D model data organization methods, geographic grids and map service specifications, and other related technologies, an intelligent urban 3D modeling model based on multisource data point cloud algorithm is designed for the two problems of unified organization and expression of urban multisource 3D model data. A point cloud preprocessing process is also designed: point cloud noise reduction and downsampling to ensure the original point cloud geometry structure remain unchanged, while improving the point cloud quality and reducing the number of point clouds. By outputting to a common 3D format, the 3D model constructed in this paper can be applied to many fields such as urban planning and design, architectural landscape design, urban management, emergency disaster relief, environmental protection, and virtual tourism. |
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AbstractList | With the rapid development of smart cities, intelligent navigation, and autonomous driving, how to quickly obtain 3D spatial information of urban buildings and build a high-precision 3D fine model has become a key problem to be solved. As the two-dimensional mapping results have constrained various needs in people’s social life, coupled with the concept of digital city and advocacy, making three-dimensional, virtualization and actualization become the common pursuit of people’s goals. However, the original point cloud obtained is always incomplete due to reasons such as occlusion during acquisition and data density decreasing with distance, resulting in extracted boundaries that are often incomplete as well. In this paper, based on the study of current mainstream 3D model data organization methods, geographic grids and map service specifications, and other related technologies, an intelligent urban 3D modeling model based on multisource data point cloud algorithm is designed for the two problems of unified organization and expression of urban multisource 3D model data. A point cloud preprocessing process is also designed: point cloud noise reduction and downsampling to ensure the original point cloud geometry structure remain unchanged, while improving the point cloud quality and reducing the number of point clouds. By outputting to a common 3D format, the 3D model constructed in this paper can be applied to many fields such as urban planning and design, architectural landscape design, urban management, emergency disaster relief, environmental protection, and virtual tourism. |
Author | Wu, Youping Zhou, Zhihui |
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Cites_doi | 10.3390/s21103493 10.1016/S1876-3804(21)60032-0 10.3390/rs13163103 |
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Copyright | Copyright © 2022 Youping Wu and Zhihui Zhou. Copyright © 2022 Youping Wu and Zhihui Zhou. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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References | L. Yichen (5) 2021; 48 Z. Juanru (9) 2021; 19 W. X. Dong (3) 2021; 21 G. Shuaikun (7) 2018; 22 B. Bin (6) 2019; 37 S. Wang (10) 2020; 6 Z. Lihui (2) 2020; 34 Z. Longqi (4) 2021; 13 L. Qiu (8) 2020; 41 H. Xiaobing (1) 2021; 35 |
References_xml | – volume: 41 start-page: 5 issue: 8 year: 2020 ident: 8 article-title: A 3D modeling method for cultural relics based on multi-source data fusion publication-title: Software contributor: fullname: L. Qiu – volume: 21 start-page: 3493 issue: 10 year: 2021 ident: 3 article-title: Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory[J] publication-title: Sensors doi: 10.3390/s21103493 contributor: fullname: W. X. Dong – volume: 19 start-page: 5 issue: 12 year: 2021 ident: 9 article-title: The key technology of multi-source data fusion to build fine 3D model publication-title: Geospatial Information contributor: fullname: Z. Juanru – volume: 6 issue: 8 year: 2020 ident: 10 article-title: Refinement modeling of buildings based on multi-source point cloud data fusion publication-title: Bulletin of Surveying and Mapping contributor: fullname: S. Wang – volume: 37 start-page: 2 issue: 2 year: 2019 ident: 6 article-title: Design and implementation of region selection algorithm for 3D point cloud model publication-title: Digital Technology and Application contributor: fullname: B. Bin – volume: 22 issue: 2 year: 2018 ident: 7 article-title: On the future development trend of smart urban landscape innovation and intelligent design publication-title: Art Education Research contributor: fullname: G. Shuaikun – volume: 35 start-page: 5 issue: 12 year: 2021 ident: 1 article-title: Application of point cloud fusion algorithm of multi-source data in 3D modeling of smart city publication-title: Beijing Surveying and Mapping contributor: fullname: H. Xiaobing – volume: 34 start-page: 5 issue: 3 year: 2020 ident: 2 article-title: 3D modeling method and accuracy analysis based on fusion of multi-source data publication-title: Beijing Surveying and Mapping contributor: fullname: Z. Lihui – volume: 48 start-page: 407 issue: 2 year: 2021 ident: 5 article-title: A method of reconstructing 3D model from 2D geological cross-section based on self-adaptive spatial sampling: A case study of Cretaceous McMurray reservoirs in a block of Canada publication-title: Petroleum Exploration and Development doi: 10.1016/S1876-3804(21)60032-0 contributor: fullname: L. Yichen – volume: 13 start-page: 3103 issue: 16 year: 2021 ident: 4 article-title: A Practical 3D Reconstruction Method for Weak Texture Scenes[J] publication-title: Remote Sensing doi: 10.3390/rs13163103 contributor: fullname: Z. Longqi |
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SubjectTerms | Accuracy Algorithms Autonomous navigation Construction Data points Disaster relief Environmental management Environmental protection Landscape architecture Landscape design Lasers Modelling Noise reduction Occlusion Smart cities Spatial data Three dimensional models Tourism Urban planning |
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Title | Intelligent City 3D Modeling Model Based on Multisource Data Point Cloud Algorithm |
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