Citation: | SHI Yunfei, LIU Kehui, LI Xiangwei, NIE Qingwei, LÜ Chunguang, ZHANG Lingling, SUN Huasheng. A 3D LOD Conceptual Model of Building with Indoor Spatial Structure and Its Generating Method[J]. Geomatics and Information Science of Wuhan University, 2022, 47(4): 561-569. DOI: 10.13203/j.whugis20200087 |
[1] |
Biljecki F, Stoter J, Ledoux H, et al. Applications of 3D City Models: State of the Art Review[J]. ISPRS International Journal of Geo-Information, 2015, 4(4): 2842-2889 doi: 10.3390/ijgi4042842
|
[2] |
赵君峤. 复杂三维建筑物模型的多细节层次自动简化方法[D]. 武汉: 武汉大学, 2012
Zhao Junqiao. Automatic Simplification Approach for the LODs of Complex 3D Building Models[D]. Wuhan: Wuhan University, 2012
|
[3] |
Gröger G, Kolbe T, Nagel C, et al. OGC City Geography Markup Language(CityGML)Encoding Standard[S]. Open Geospatial Consortium, 2012
|
[4] |
Hagedorn B, Trapp M, Glander T, et al. Towards an Indoor Level-of-Detail Model for Route Visualization [C]//The 10th International Conference on Mobile Data Management: Systems, Services and Middle-ware, Taipei, Taiwan, China, 2009
|
[5] |
Abdul-Rahman A, Zlatanova S, Coors V. Innovations in 3D Geo Information Systems[M]. Heidelberg: Springer, 2006
|
[6] |
Boeters R. Automatic Enhancement of CityGML LOD2 Models with Interiors and Its Usability for Net Internal Area Determination[D]. Delft: Delft University of Technology, 2013
|
[7] |
Löwner M O, Benner J, Gröger G, et al. New Concepts for Structuring 3D City Models —An Extended Level of Detail Concept for CityGML Buildings[C]// The 13th International Conference Computational Science and Its Applications, Malaga, Spain, 2013
|
[8] |
Benner J, Geiger A, Gröger G, et al. Enhanced LOD Concepts for Virtual 3D City Models[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013, 2 (W1): 51-61
|
[9] |
Biljecki F, Ledoux H, Stoter J, et al. Formalisation of the Level of Detail in 3D City Modelling[J]. Computers, Environment and Urban Systems, 2014, 48: 1-15 doi: 10.1016/j.compenvurbsys.2014.05.004
|
[10] |
Tang L, Ying S, Li L, et al. An Application-Driven LOD Modeling Paradigm for 3D Building Models [J]. ISPRS Journal of Photogrammetry and Re⁃ mote Sensing, 2020, 161: 194-207 doi: 10.1016/j.isprsjprs.2020.01.019
|
[11] |
Biljecki F. Level of Detail in 3D City Models[D]. Delft: Delft University of Technology, 2017
|
[12] |
Lindstrom P, Turk G. Image-Driven Simplification [J]. ACM Transactions on Graphics, 2000, 19(3): 204-241 doi: 10.1145/353981.353995
|
[13] |
Park I, Shirani S, Capson D W. Mesh Simplification Using an Area-Based Distortion Measure[J]. Journal of Mathematical Modelling and Algo⁃ rithms, 2006, 5(3): 309-329 doi: 10.1007/s10852-005-9036-8
|
[14] |
van Oosterom P, Zlatanova S, Penninga F, et al. Advances in 3D Geoinformation Systems[M]. Heidelberg: Springer, 2008
|
[15] |
应申, 陈乃镔, 李威阳, 等. 三维房产群集对象可视化方法[J]. 武汉大学学报·信息科学版, 2020, 45(1): 81-88 doi: 10.13203/j.whugis20190242
Ying Shen, Chen Naibin, Li Weiyang, et al. Visualization Methods for the Coherent Set of 3D Building Property Units[J]. Geomatics and Informa⁃ tion Science of Wuhan University, 2020, 45(1): 81-88 doi: 10.13203/j.whugis20190242
|
[16] |
Ying S, Chen N B, Li W Y, et al. Distortion Visualization Techniques for 3D Coherent Sets: A Case Study of 3D Building Property Units[J]. Computers, Environment and Urban Systems, 2019, 78: 101382 doi: 10.1016/j.compenvurbsys.2019.101382
|
[17] |
Palmer R S, Shapiro V. Chain Models of Physical Behavior for Engineering Analysis and Design[J]. Research in Engineering Design, 1993, 5(3): 161-184
|
[18] |
Palmer R S. Chain Models and Finite Element Analysis: An Executable Chains Formulation of Plane Stress[J]. Computer Aided Geometric Design, 1995, 12(7): 733-770 doi: 10.1016/0167-8396(95)00015-X
|
[19] |
Egli R, Stewart N F. A Framework for System Specification Using Chains on Cell Complexes[J]. Computer-Aided Design, 2000, 32(7): 447-459 doi: 10.1016/S0010-4485(00)00024-5
|
[20] |
王永志. 基于胞腔复形链的地下空间对象三维表达与分析计算统一数据模型研究[D]. 南京: 南京师范大学, 2012
Wang Yongzhi. Research on the Unified Data Model of Three-Dimensional Representation and Analysis Calculation of Underground Spatial Objects Based on the Cell Complex Chain[D]. Nanjing: Nanjing Normal University, 2012
|
[21] |
Di Carlo A, Milicchio F, Paoluzzi A, et al. Chain-Based Representations for Solid and Physical Modeling[J]. IEEE Transactions on Automation Science and Engineering, 2009, 6(3): 454-467 doi: 10.1109/TASE.2009.2021342
|
[22] |
张玲玲, 史云飞, 吕春光, 等. 顾及拓扑的室内薄壁三维模型重建方法[J]. 测绘科学, 2019, 44(12): 141-146 https://www.cnki.com.cn/Article/CJFDTOTAL-CHKD201912021.htm
Zhang Lingling, Shi Yunfei, Lü Chunguang, et al. A Method for Reconstructing Indoor Thin-Wall 3D Model Considering Topology[J]. Science of Surveying and Mapping, 2019, 44(12): 141-146 https://www.cnki.com.cn/Article/CJFDTOTAL-CHKD201912021.htm
|
[23] |
史云飞, 卞西蜀, 张永翔, 等. 兼顾语义的室内3维模型自动重建方法[J]. 导航定位学报, 2020, 8(1): 9-14 https://www.cnki.com.cn/Article/CJFDTOTAL-CHWZ202001002.htm
Shi Yunfei, Bian Xishu, Zhang Yongxiang, et al. An Automatic Reconstruction Method for Indoor Three-Dimensional Model Considering Semantics [J]. Journal of Navigation and Positioning, 2020, 8(1): 9-14 https://www.cnki.com.cn/Article/CJFDTOTAL-CHWZ202001002.htm
|
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