ZHANG Hongxin, FANG Yutong, LIU Mushui, DONG Binzhi. Dual Recognition Method of Spatial Layout Fusion for Complex Architectural Plan Drawings[J]. Geomatics and Information Science of Wuhan University, 2021, 46(9): 1354-1361. DOI: 10.13203/j.whugis20210323
Citation: ZHANG Hongxin, FANG Yutong, LIU Mushui, DONG Binzhi. Dual Recognition Method of Spatial Layout Fusion for Complex Architectural Plan Drawings[J]. Geomatics and Information Science of Wuhan University, 2021, 46(9): 1354-1361. DOI: 10.13203/j.whugis20210323

Dual Recognition Method of Spatial Layout Fusion for Complex Architectural Plan Drawings

Funds: 

The National Natural Science Foundation of China U1909204

More Information
  • Author Bio:

    ZHANG Hongxin, PhD, associate professor, specializes in computer graphics, cloud computing and artificial intelligence. E-mail: zhx@cad.zju.edu.cn

  • Received Date: June 11, 2021
  • Published Date: September 17, 2021
  •   Objectives  The spatial layout of vectorized complex architectural drawings is widely used in 5G base station construction, smart homes, and AR/VR(augmented reality/virtual reality).
      Methods  To solve the difficulty of identifying the spatial layout of drawings caused by various irregular drawing elements, a high?performance fusion recognition method combining raster image and vector representation is proposed. First, the rasterized representation of architectural plan drawings is used to extract the main development direction, and several enclosed spaces are constructed. Then, the vectorized representation of architectural plan drawings is used and according to the directional and adjacency characteristics of space recognition, the half?wall structure is innovatively proposed to calculate geometric position and topological relationship. The wall layout can be rebuilt as a whole with high precision according to the principle of space duality. Finally, the proposed method and the traditional methods are analyzed on the vector building plan data set of various wall layouts.
      Results  The experimental results show that the proposed fusion dual recognition algorithm is usable and effective for various types of building models.
      Conclusions  It has higher robustness and is less interfered by specific architectural drawing types.
  • [1]
    Yin X T, Wonka P, Razdan A. Generating 3D Building Models from Architectural Drawings: A Survey[J]. IEEE Computer Graphics and Applications, 2009, 29(1): 20-30 doi: 10.1109/MCG.2009.9
    [2]
    Yang J, Jang H, Kim J, et al. Semantic Segmentation in Architectural Floor Plans for Detecting Walls and Doors[C]//11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics(CISP-BMEI), Beijing, China, 2018
    [3]
    Zeng Z L, Li X Z, Yu Y K, et al. Deep Floor Plan Recognition Using a Multi-Task Network with Room-Boundary-Guided Attention [C]//IEEE/ CVF International Conference on Computer Vision (ICCV), Seoul, South Korea, 2019
    [4]
    Lewis R, Séquin C. Generation of 3D Building Models from 2D Architectural Plans[J]. Computer-Aided Design, 1998, 30(10): 765-779 doi: 10.1016/S0010-4485(98)00031-1
    [5]
    Dosch P, Tombre K, Ah-Soon C, et al. A Complete System for the Analysis of Architectural Drawings [J]. International Journal on Document Analysis and Recognition, 2000, 3(2): 102-116 doi: 10.1007/PL00010901
    [6]
    Gimenez L, Robert S, Suard F, et al. Automatic Reconstruction of 3D Building Models from Scanned 2D Floor Plans[J]. Automation in Construction, 2016, 63: 48-56 doi: 10.1016/j.autcon.2015.12.008
    [7]
    Or S H, Wong K H, Yu Y K, et al. Highly Automatic Approach to Architectural Floorplan Image Understanding & Model Generation [C]// VMV2005, Erlangen, Germany, 2005
    [8]
    Ahmed S, Liwicki M, Weber M, et al. Improved Automatic Analysis of Architectural Floor Plans [C]//2011 International Conference on Document Analysis and Recognition, Beijing, China, 2011
    [9]
    Macé S, Locteau H, Valveny E, et al. A System to Detect Rooms in Architectural Floor Plan Images [C]//Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, Boston, Massachusetts, USA, 2010
    [10]
    Rendek J, Masini G, Dosch P, et al. The Search for Genericity in Graphics Recognition Applications: Design Issues of the Qgar Software System[C]//Document Analysis Systems Ⅵ, Berlin, Germany, 2004
    [11]
    Heras L P, Ahmed S, Liwicki M, et al. Statistical Segmentation and Structural Recognition for Floor Plan Interpretation[J]. International Journal on Document Analysis and Recognition, 2014, 17(3): 221-237 doi: 10.1007/s10032-013-0215-2
    [12]
    Jang H, Yu K, Yang J. Indoor Reconstruction from Floorplan Images with a Deep Learning Approach [J]. ISPRS International Journal of Geo Information, 2020, 9(2): 65 doi: 10.3390/ijgi9020065
    [13]
    Dodge S, Xu J, Stenger B. Parsing Floor Plan Images [C]//2017 Fifteenth IAPR International Conference on Machine Vision Applications(MVA), Nagoya, Japan, 2017
    [14]
    Ahmed S, Liwicki M, Weber M, et al. Automatic Room Detection and Room Labeling from Architectural Floor Plans[C]//201210th IAPR International Workshop on Document Analysis Systems, Gold Coast, Australia, 2012
    [15]
    So C, Baciu G, Sun H Q. Reconstruction of 3D Virtual Buildings from 2D Architectural Floor Plans [C]//Proceedings of the ACM Symposium on Virtual Reality Software and Technology, Taipei, Taiwan, China, 1998
    [16]
    Lu T, Tai C L, Su F, et al. A New Recognition Model for Electronic Architectural Drawings[J]. Computer-Aided Design, 2005, 37(10): 1 053-1 069 doi: 10.1016/j.cad.2004.11.004
    [17]
    Boeters R, Ohori K A, Biljecki F, et al. Automatically Enhancing CityGML LOD2 Models with a Corresponding Indoor Geometry[J]. International Journal of Geographical Information Science, 2015, 29(12): 2 248-2 268 doi: 10.1080/13658816.2015.1072201
    [18]
    Goetz M, Zipf A. Towards Defining a Framework for the Automatic Derivation of 3D CityGML Models from Volunteered Geographic Information[J]. International Journal of 3-D Information Modeling, 2012, 1(2): 1-16 doi: 10.4018/ij3dim.2012040101
    [19]
    侯绍洋, 赵学胜, 官亚勤. 利用多分辨率半边进行全球多分辨率DEM无缝表达[J]. 武汉大学学报·信息科学版, 2018, 43(3): 372-378 doi: 10.13203/j.whugis20150152

    Hou Shaoyang, Zhao Xuesheng, Guan Yaqin. Seamless Expression of Global Multi-Resolution DEM Based on Multi-Resolution Half-Edges Structure[J]. Geomatics and Information Science of Wuhan University, 2018, 43(3): 372-378 doi: 10.13203/j.whugis20150152
    [20]
    Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation [M]//Lecture Notes in Computer Science. Cham: Springer, 2015
    [21]
    Bresenham J E. Algorithm for Computer Control of a Digital Plotter[J]. IBM Systems Journal, 1965, 4(1): 25-30 doi: 10.1147/sj.41.0025
  • Related Articles

    [1]WU Chunjun, SUN Yueqiang, WANG Xianyi, BAI Weihua, MENG Xiangguang, DU Qifei, WANG Dongwei, LI Fu. Adjustment of GPS Flex Power and Its Interference Analysis Based on FY-3D Satellite[J]. Geomatics and Information Science of Wuhan University, 2023, 48(5): 687-693. DOI: 10.13203/j.whugis20200569
    [2]Yin Gang, Zhang Yingtang, Shi Zhiyong, Li Zhining. Real-time Compensation Method of Magnetic Heading Perturbations Based on Magnetic Anomaly Inversion[J]. Geomatics and Information Science of Wuhan University, 2016, 41(7): 978-982. DOI: 10.13203/j.whugis20140260
    [3]HUANG Shuqiang, FU Zhongliang. A Channel Assignment Algorithm Based on Interference Avoiding in Wireless Mesh Networks[J]. Geomatics and Information Science of Wuhan University, 2013, 38(2): 248-251.
    [4]ZHANG Bo, ZHANG Hong, WANG Ziwei, WANG Chao. Electromagnetic Model Used for Building Height Retrieval by Single High Resolution SAR Image[J]. Geomatics and Information Science of Wuhan University, 2012, 37(12): 1460-1463.
    [5]GUO Wenfei, ZHENG Jiansheng, ZHANG Tisheng, LI Chaoran. A Space-Frequency Adaptive Processing Algorithm for GPS Radio Frequency Interference Suppression[J]. Geomatics and Information Science of Wuhan University, 2011, 36(11): 1348-1352.
    [6]HAN Tianzhu, CAO Jianping, LU Mingquan. Anti-interference Antenna Based Near-Far Effect Mitigation Method[J]. Geomatics and Information Science of Wuhan University, 2011, 36(10): 1222-1225.
    [7]ZHAO Yang, LI Guangxia, CHANG Jiang, LIU Yun. Research on Electromagnetic Environment of Satellite Navigation in S-band[J]. Geomatics and Information Science of Wuhan University, 2011, 36(10): 1213-1217.
    [8]YIN Hui, ZHANG Xiaohong, ZHANG Xiaowu, LIU Xingfa. Interference Analysis to Aerial Flight Caused by UHV Lines Using Airborne GPS[J]. Geomatics and Information Science of Wuhan University, 2009, 34(7): 774-777.
    [9]HUANG Motao, ZHAI Guojun, OUYANG Yongzhong, REN Laiping. On Error Compensation in Marine Magnetic Survey[J]. Geomatics and Information Science of Wuhan University, 2006, 31(7): 603-606.
    [10]Li Shaoxin. Magnetic Monopoles, Maxwell's Equations and Electromagnetic Picture[J]. Geomatics and Information Science of Wuhan University, 1987, 12(3): 86-90.

Catalog

    Article views (1507) PDF downloads (109) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return