A Systematic Review of Image-Based Technologies for Detecting As-Is BIM Objects
Publication: Computing in Civil Engineering 2021
ABSTRACT
Despite many potential applications of building information modeling (BIM), the automated evaluation of existing as-is information in images and its transformation into purpose-oriented digital as-is BIM models is still under investigation. Although several previous studies have systematically investigated technologies for creating as-is BIM models for existing building elements, they do not include the latest approaches to object recognition in terms of machine learning and deep learning methods. Many researchers point out the need for more efficient and feasible algorithms to provide an automatic and information-rich BIM model generation system. By following a systematic assessment methodology, this review paper provides an overview of current object recognition and detection strategies in the architecture, engineering, construction, and operation (AECO) domain and discusses their potentials and limitations based on the richness of semantic information with respect to as-is BIM generation of existing buildings. The combined application of image-based object detection and the automated transfer to BIM will be investigated in future work.
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REFERENCES
Acharya, D., Khoshelham, K., and Winter, S. (2019). “BIM-PoseNet: Indoor camera localisation using a 3D indoor model and deep learning from synthetic images.” ISPRS Journal of Photogrammetry and Remote Sensing, 150, 245–258.
Adán, A., Quintana, B., Prieto, S. A., and Bosché, F. (2018). “Scan-to-BIM for ‘secondary’ building components.” Advanced Engineering Informatics, 37, 119–138.
Amano, K., and Lou, E. (2016). “BIM for existing facilities: feasibility of spectral image integration to 3D point cloud data.” MATEC Web Conf., 2016, 1–5.
Braun, A., and Borrmann, A. (2019). “Combining inverse photogrammetry and BIM for automated labeling of construction site images for machine learning.” Automation in Construction, 106, 102879.
Chen, K., Lu, W., Peng, Y., Rowlinson, S., and Huang, G. Q. (2015). “Bridging BIM and building: From a literature review to an integrated conceptual framework.” Int. J. Constr. Manag., 33(6), 1405–1416.
Chen, L., Lu, Q., and Zhao, X. (2019). “A Semi-Automatic Image-based Object Recognition System for Constructing As-is IFC BIM Objects based on Fuzzy-MAUT.” Int. J. Constr. Manag., 1–15.
Dang, N. S., and Shim, C. S. (2018). “BIM authoring for an image-based bridge maintenance system of existing cable-supported bridges.” IOP Conf. Ser.: Earth Environ. Sci., 143, 12032.
Dimitrov, A., and Golparvar-Fard, M. (2014). “Vision-based material recognition for automated monitoring of construction progress and generating building information modeling from unordered site image collections.” Adv. Eng. Inform., 28(1), 37–49.
Ferguson, M., and Law, K. (2019). “A 2D-3D Object Detection System for Updating Building Information Models with Mobile Robots.” 2019 IEEE (WACV), https://doi.org/10.1109/WACV.2019.00149.
Han, K. K., and Golparvar-Fard, M. (2015). “Appearance-based material classification for monitoring of operation-level construction progress using 4D BIM and site photologs.” Autom. Constr., 53, 44–57.
He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017). “Mask R-CNN.”, IEEE International Conference on Computer Vision; Institute of Electrical and Electronics Engineers; ICCV, Piscataway, NJ, 2961–2968.
Kemker, R., Salvaggio, C., and Kanan, C. (2018). “Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning.” ISPRS Journal of Photogrammetry and Remote Sensing, 145(1–2), 60–77.
Kim, J., Song, J., and Lee, J.-K. (2019). “Recognizing and Classifying Unknown Object in BIM Using 2D CNN.” Computer-Aided Architectural Design. “Hello, Culture”, J.-H. Lee, ed., Springer Singapore, Singapore, 47–57.
Kropp, C., and Koch, C. (2013). “Objekterkennung in BIM-registrierten Videos zur Zustandserfassung im Innenausbau.” 25. Forum Bauinformatik 2013, https://doi.org/10.1515/9783110924992-003, 359–369.
Kropp, C., Koch, C., and König, M. (2018). “Interior construction state recognition with 4D BIM registered image sequences.” Autom. Constr., 86, 11–32.
Li, W., He, C., Fang, J., Zheng, J., Fu, H., and Yu, L. (2019). “Semantic Segmentation-Based Building Footprint Extraction Using Very High-Resolution Satellite Images and Multi-Source GIS Data.” Remote Sens., 11(4), 403.
Li, Y., Li, W., Tang, S., Darwish, W., Hu, Y., and Chen, W. (2020). “Automatic Indoor as-Built Building Information Models Generation by Using Low-Cost RGB-D Sensors.” Sensors 2020 (Basel, Switzerland), 20(1), 293.
Lu, Q., and Lee, S. (2016). “Development of a Semi-Automatic Image-based Object Recognition System for Reconstructing As-is BIM Objects based on Fuzzy Multi-Attribute Utility Theory.” The CIB W78 Conference 2016.
Lu, Q., and Lee, S. (2017). “Image-Based Technologies for Constructing As-Is Building Information Models for Existing Buildings.” J. Comput. Civ. Eng., 31(4), 4017005.
Lu, Q. C., and Lee, S. H. (2015). “Comparative analysis of technologies and methods for automatic construction of building information models for existing buildings.” ICE HKA Annual Conference 2015, 181–190.
Marmanis, D., Wegner, J. D., Galliani, S., Schindler, K., Datcu, M., and Stilla, U. (2016). “Semantic Segmentation of Aerial Images with an Ensemble of CNNs.” ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 473–480.
Moher, D., Liberati, A., Tetzlaff, J., and Altman, D. G. (2009). “Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement.” PLoS Med 6(7): e1000097 doi:https://doi.org/10.1371/journal.pmed.1000097.
Mou, L., Hua, Y., and Zhu, X. (2019). “A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes.” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12408–12417.
Oskouie, P., Becerik-Gerber, B., and Soibelman, L. (2017). “Automated Recognition of Building Façades for Creation of As-Is Mock-Up 3D Models.” J. Comput. Civ. Eng., 31(6), 4017059.
Sitaula, C., Xiang, Y., Zhang, Y., Lu, X., and Aryal, S. (2019). “Indoor image representation by high-level semantic features.” IEEE Access, 7, 84967–84979.
Sun, L., Tang, Y., and Zhang, L. (2017). “Rural Building Detection in High-Resolution Imagery Based on a Two-Stage CNN Model.” IEEE Geosci. Remote Sensing Lett., 14, 1998–2002.
Tang, F., Han, C., Ma, T., Chen, T., and Jia, Y. (2021). “Quantitative analysis and visual presentation of segregation in asphalt mixture based on image processing and BIM.” Autom. Constr., 121, 103461.
Troncoso-Pastoriza, F., Eguía-Oller, P., Díaz-Redondo, R. P., and Granada-Álvarez, E. (2018). “Generation of BIM data based on the automatic detection, identification and localization of lamps in buildings.” Sustainable Cities and Society, 36, 59–70.
Valero, E., Forster, A., Bosché, F., Renier, C., Hyslop, E., and Wilson, L. (2018). “High Level-of-Detail BIM and Machine Learning for Automated Masonry Wall Defect Surveying.” Proceedings of the 35th ISARC, Berlin, Germany, 740–747.
Vandecasteele, F., Merci, B., and Verstockt, S. (2017). “Fireground location understanding by semantic linking of visual objects and building information models.” Fire Safety Journal, 91, 1026–1034.
Volk, R., Stengel, J., and Schultmann, F. (2014). “Building Information Modeling (BIM) for existing buildings — Literature review and future needs.” Autom. Constr., 38, 109–127.
Xue, F., Lu, W., and Chen, K. (2018). “Automatic Generation of Semantically Rich As-Built Building Information Models Using 2D Images: A Derivative-Free Optimization Approach.” Computer-Aided Civil and Infrastructure Engineering, 33(11), 926–942.
Yang, J., Shi, Z.-K., and Wu, Z.-Y. (2016). “Towards automatic generation of as-built BIM: 3D building facade modeling and material recognition from images.” Int. J. Autom. Comput., 13(4), 338–349.
Yi, Y., Zhang, Z., Zhang, W., Zhang, C., Li, W., and Zhao, T. (2019). “Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network.” Remote Sensing, 11(15), 1774.
Zhan, J., Ge, X. J., Huang, S., Zhao, L., Wong, J. K. W., and He, S. X. (2019). “Improvement of the inspection-repair process with building information modelling and image classification.” Facilities, 37(7/8), 395–414.
Zhang, Q., Wang, Y., Liu, Q., Liu, X., and Wang, W. (2016). “CNN based suburban building detection using monocular high resolution Google Earth images.” IGARSS, 661–664.
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Computing in Civil Engineering 2021
Pages: 498 - 505
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Published online: May 24, 2022
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