多源多尺度实景三维模型融合技术探究
Exploration of Multi-Source and Multi-Scale Realistic 3D Model Fusion Techniques
DOI: 10.12677/jisp.2024.133026, PDF,    科研立项经费支持
作者: 管洪涛, 何 勇*：重庆交通大学智慧城市学院，重庆
关键词: 实景三维；多源异构；场景融合；数据轻量化；Realistic Three-Dimensional； Multi-Source Heterogeneous； Scene Fusion； Data Lightweight
摘要: 在城市级实景三维建设中，融合多源、多尺度的实景三维数据具有关键性意义。本研究致力于探索应对海量、异构、多尺度实景三维数据的融合技术方法。通过改进空间精度匹配和数据接边的方法，结合实景三维数据成果的标准化和轻量化处理，本研究有效提升了实景三维场景的调度和显示效率。这些技术改进不仅丰富了实景三维场景的细节表达，还提高了其完整性，为城市级实景三维建设提供了更为可靠和高效的技术支持。
Abstract: Fusion of multi-source and multi-scale real-world 3D data is of key significance in city-level real-world 3D construction. This study is dedicated to exploring the fusion technology methods to cope with massive, heterogeneous and multi-scale real-view 3D data. By improving the methods of spatial accuracy matching and data joining, combined with the standardization and lightweight processing of real-life 3D data results, this study effectively improves the scheduling and display efficiency of real-life 3D scenes. These technical improvements not only enrich the detail expression of the real-life 3D scene, but also improve its integrity, providing more reliable and efficient technical support for city-level real-life 3D construction.
文章引用：管洪涛, 何勇. 多源多尺度实景三维模型融合技术探究[J]. 图像与信号处理, 2024, 13(3): 302-310. https://doi.org/10.12677/jisp.2024.133026

参考文献

[1] 张艳玲, 安乐, 石磊. BIM + GIS技术在城市建筑三维可视化中的应用研究[J]. 建设科技, 2024(2): 65-67.
[2] 赖建民, 卜鹏飞. 三维激光扫描技术在建筑物三维建模可视化中的应用探究[J]. 科技创新与应用, 2024, 14(1): 193-196.
[3] 刘刚, 陈根深, 陈麒玉, 等. 基于Web的大规模三维空间模型高效可视化关键技术[J]. 地理空间信息, 2023, 21(12): 8-13.
[4] 肖建华, 李海亭, 李鹏鹏, 等. 实景三维的内涵与分类分级[J]. 城市勘测, 2021(5): 5-10.
[5] Gimenez, L., Hippolyte, J., Robert, S., Suard, F. and Zreik, K. (2015) Review: Reconstruction of 3D Building Information Models from 2D Scanned Plans. Journal of Building Engineering, 2, 24-35. [Google Scholar] [CrossRef] 
[6] Shalma, H. and Selvaraj, P. (2023) A Review on 3D Image Reconstruction on Specific and Generic Objects. Materials Today: Proceedings, 80, 2400-2405. [Google Scholar] [CrossRef] 
[7] Wu, X., Ou, J., Wen, Y., Liu, X., He, J. and Zhang, J. (2022) Developing a Data-Fusing Method for Mapping Fine-Scale Urban Three-Dimensional Building Structure. Sustainable Cities and Society, 80, Article ID: 103716. [Google Scholar] [CrossRef] 
[8] Fadhel, M.A., Duhaim, A.M., Saihood, A., Sewify, A., Al-Hamadani, M.N.A., Albahri, A.S., et al. (2024) Comprehensive Systematic Review of Information Fusion Methods in Smart Cities and Urban Environments. Information Fusion, 107, Article ID: 102317. [Google Scholar] [CrossRef] 
[9] Ma, Z. and Liu, S. (2018) A Review of 3D Reconstruction Techniques in Civil Engineering and Their Applications. Advanced Engineering Informatics, 37, 163-174. [Google Scholar] [CrossRef] 
[10] Wu, J., Wyman, O., Tang, Y., Pasini, D. and Wang, W. (2024) Multi-view 3D Reconstruction Based on Deep Learning: A Survey and Comparison of Methods. Neurocomputing, 582, Article ID: 127553. [Google Scholar] [CrossRef] 
[11] Xu, J., Shu, X., Qiao, P., Li, S. and Xu, J. (2023) Developing a Digital Twin Model for Monitoring Building Structural Health by Combining a Building Information Model and a Real-Scene 3D Model. Measurement, 217, Article ID: 112955. [Google Scholar] [CrossRef] 
[12] Flores-Fuentes, W., Trujillo-Hernández, G., Alba-Corpus, I.Y., Rodríguez-Quiñonez, J.C., Mirada-Vega, J.E., Hernández-Balbuena, D., et al. (2023) 3D Spatial Measurement for Model Reconstruction: A Review. Measurement, 207, Article ID: 112321. [Google Scholar] [CrossRef] 
[13] Ingale, A.K. and J., D.U. (2021) Real-Time 3D Reconstruction Techniques Applied in Dynamic Scenes: A Systematic Literature Review. Computer Science Review, 39, Article ID: 100338. [Google Scholar] [CrossRef] 
[14] Chen, H., Han, Q. and De Vries, B. (2020) Modeling the Spatial Relation between Urban Morphology, Land Surface Temperature and Urban Energy Demand. Sustainable Cities and Society, 60, Article ID: 102246. [Google Scholar] [CrossRef] 
[15] Garde, A. and Hoff, A. (2017) Zoning Reform for Advancing Sustainability: Insights from Denver’s Form-Based Code. Journal of Urban Design, 22, 845-865. [Google Scholar] [CrossRef] 
[16] Wentz, E.A., York, A.M., Alberti, M., Conrow, L., Fischer, H., Inostroza, L., et al. (2018) Six Fundamental Aspects for Conceptualizing Multidimensional Urban Form: A Spatial Mapping Perspective. Landscape and Urban Planning, 179, 55-62. [Google Scholar] [CrossRef] 
[17] Zhu, Z., Zhou, Y., Seto, K.C., Stokes, E.C., Deng, C., Pickett, S.T.A., et al. (2019) Understanding an Urbanizing Planet: Strategic Directions for Remote Sensing. Remote Sensing of Environment, 228, 164-182. [Google Scholar] [CrossRef] 
[18] Schmidhuber, J. (2015) Deep Learning in Neural Networks: An Overview. Neural Networks, 61, 85-117. [Google Scholar] [CrossRef] [PubMed]
[19] Reddy, A. and Leslie, T.F. (2015) Volume per Capita as a Useful Measure of Residential Space. Urban Geography, 36, 1099-1112. [Google Scholar] [CrossRef] 
[20] Tournaire, O., Brédif, M., Boldo, D. and Durupt, M. (2010) An Efficient Stochastic Approach for Building Footprint Extraction from Digital Elevation Models. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 317-327. [Google Scholar] [CrossRef] 
[21] Weissgerber, F., Colin-Koeniguer, E., Nicolas, J. and Trouvé, N. (2017) 3D Monitoring of Buildings Using Terrasar-X Insar, Dinsar and Polsar Capacities. Remote Sensing, 9, Article 1010. [Google Scholar] [CrossRef]