Modeling of 3D geometry uncertainty in Scan-to-BIM automatic indoor reconstruction
Tài liệu tham khảo
Herle, 2020, GIM and BIM, PFG J. Photogramm. Remote Sens. Geoinforma. Sci., 88, 33
Azhar, 2011, Building information modeling (BIM): trends, benefits, risks, and challenges for the AEC industry, Leadersh. Manag. Eng., 11, 241, 10.1061/(ASCE)LM.1943-5630.0000127
Li, 2014, A project-based quantification of BIM benefits, Int. J. Adv. Robot. Syst., 11, 123, 10.5772/58448
Czerniawski, 2020, Automated digital modeling of existing buildings: A review of visual object recognition methods, Autom. Constr., 113, 10.1016/j.autcon.2020.103131
Nikoohemat, 2020, Indoor 3D reconstruction from point clouds for optimal routing in complex buildings to support disaster management, Autom. Constr., 113, 1, 10.1016/j.autcon.2020.103109
Biljecki, 2015, Propagation of positional error in 3D GIS: estimation of the solar irradiation of building roofs, Int. J. Geogr. Inf. Sci., 29, 2269, 10.1080/13658816.2015.1073292
Hajian, 2010, Scan to BIM: factors affecting operational and computational errors and productivity loss, Int. Symp. Automation Robot. Construct., 27, 265
Tran, 2019, Geometric comparison and quality evaluation of 3D models of indoor environments, ISPRS J. Photogramm. Remote Sens., 149, 29, 10.1016/j.isprsjprs.2019.01.012
Khoshelham, 2021, Results of the ISPRS benchmark on indoor modeling, ISPRS Open J. Photogrammet. Remote Sensing, 2, 10.1016/j.ophoto.2021.100008
Tang, 2010, Automatic reconstruction of as-built building information models from laser-scanned point clouds: a review of related techniques, Autom. Constr., 19, 829, 10.1016/j.autcon.2010.06.007
Pătrăucean, 2015, State of research in automatic as-built modeling, Adv. Eng. Inform., 162, 10.1016/j.aei.2015.01.001
Pintore, 2020, State-of-the-art in Automatic 3D Reconstruction of Structured Indoor Environments, EUROGRAPHICS, 39
Son, 2015, Scan-to-BIM-an overview of the current state of the art and a look ahead, Proc. Int. Symp. Automation Robotics Construct., 32, 1
Kalantari, 2016, Accuracy and utility of the structure sensor for collecting 3D indoor information. Geospatial Information, Science, 19, 202
Nikoohemat, 2018, Semantic interpretation of mobile laser scanner point clouds in indoor scenes using trajectories, Remote Sens., 10, 1754, 10.3390/rs10111754
Meyer, 2022, Change detection for indoor construction progress monitoring based on BIM, point clouds and uncertainties, Autom. Constr., 141, 10.1016/j.autcon.2022.104442
Maalek, 2019, Automatic recognition of common structural elements from point clouds for automated Progress monitoring and dimensional quality control in reinforced concrete construction, Remote Sens., 11, 1102, 10.3390/rs11091102
Zhenhua, 2009, Comparison of optical sensor-based spatial data collection techniques for civil infrastructure Modeling, J. Comput. Civ. Eng., 23, 170, 10.1061/(ASCE)0887-3801(2009)23:3(170)
Soudarissanane, 2011, Scanning geometry: influencing factor on the quality of terrestrial laser scanning points, ISPRS J. Photogramm. Remote Sens., 66, 389, 10.1016/j.isprsjprs.2011.01.005
Golparvar-Fard, 2011, Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques, Autom. Constr., 20, 1143, 10.1016/j.autcon.2011.04.016
Wujanz, 2018, Determination of intensity-based stochastic models for terrestrial laser scanners utilising 3D-point clouds, Sensors, 18, 7, 10.3390/s18072187
Tan, 2018, Investigation of TLS intensity data and distance measurement errors from target specular reflections, Remote Sens., 10, 1077, 10.3390/rs10071077
Koch, 2008, Evaluation of uncertainties in measurements by Monte Carlo simulations with an application for laser scanning, J. Appl. Geod., 2
Lichti, 2007, Error modeling, calibration and analysis of an AM-CW terrestrial laser scanner system, ISPRS J. Photogramm. Remote Sens., 61, 307, 10.1016/j.isprsjprs.2006.10.004
Holst, 2018, Dealing with systematic laser scanner errors due to misalignment at area-based deformation analyses, J. Appl. Geod., 12, 169, 10.1515/jag-2017-0044
Mura, 2014, Automatic room detection and reconstruction in cluttered indoor environments with complex room layouts, Comput. Graph., 44, 20, 10.1016/j.cag.2014.07.005
Khoshelham, 2014, 3D modeling of interior spaces: learning the language of indoor architecture, Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci., 40, 321, 10.5194/isprsarchives-XL-5-321-2014
Previtali, 2018, Indoor building reconstruction from occluded point clouds using graph-cut and ray-tracing, Appl. Sci., 8, 1529, 10.3390/app8091529
Jung, 2018, Automated 3D volumetric reconstruction of multiple-room building interiors for as-built BIM, Adv. Eng. Inform., 38, 811, 10.1016/j.aei.2018.10.007
Yang, 2019, Automatic indoor reconstruction from point clouds in multi-room environments with curved walls, Sensors, 19, 17
Xiong, 2013, Automatic creation of semantically rich 3D building models from laser scanner data, Autom. Constr., 31, 325, 10.1016/j.autcon.2012.10.006
Quintana, 2016, Semantic scan planning for indoor structural elements of buildings, Advance Engineering, Information, 30, 643
Bassier, 2018, IFC wall reconstruction from unstructured point clouds, 33
Ochmann, 2019, Automatic reconstruction of fully volumetric 3D building models from oriented point clouds, ISPRS J. Photogramm. Remote Sens., 151, 251, 10.1016/j.isprsjprs.2019.03.017
Jarząbek-Rychard, 2022, Automatic enrichment of indoor 3D models using a deep learning approach based on single images with unknown camera poses, ISPRS Ann. Photogram. Remote Sens. Spatial Informa. Sci., 8, 1
Previtali, 2018, Towards automatic reconstruction of indoor scenes from incomplete point clouds: door and window detection and regularization, Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci., XLII-4, 507, 10.5194/isprs-archives-XLII-4-507-2018
Tran, 2020, Procedural reconstruction of 3D indoor models from lidar data using reversible jump markov chain Monte Carlo, Remote Sens., 12, 838, 10.3390/rs12050838
Esfahani, 2021, Quantitative investigation on the accuracy and precision of scan-to-BIM under different modeling scenarios, Autom. Constr., 126, 10.1016/j.autcon.2021.103686
Xiao, 2014, Reconstructing the world’s museums, Int. J. Comput. Vis., 110, 243, 10.1007/s11263-014-0711-y
Becker, 2015, Grammar-supported 3D indoor reconstruction from point clouds for “as-built” BIM, 17
Tran, 2017, Extracting topological relations between indoor spaces from point clouds, 401
Díaz-Vilariño, 2015, 3D modeling of building indoor spaces and closed doors from imagery and point clouds, Sensors, 15, 3491, 10.3390/s150203491
Mura, 2016, Piecewise-planar reconstruction of multi-room interiors with arbitrary wall arrangements, Comput. Graphics Forum, 35, 179, 10.1111/cgf.13015
Macher, 2017, From point clouds to building information models: 3D semi-automatic reconstruction of indoors of existing buildings, Appl. Sci., 7, 1030, 10.3390/app7101030
Lehtola, 2017, Comparison of the selected state-of-the-art 3D indoor scanning and point cloud generation methods, Remote Sens., 9, 796, 10.3390/rs9080796
Fang, 2021, Floorplan generation from 3D point clouds: A space partitioning approach, ISPRS J. Photogramm. Remote Sens., 175, 44, 10.1016/j.isprsjprs.2021.02.012
Tang, 2022, BIM generation from 3D point clouds by combining 3D deep learning and improved morphological approach, Autom. Constr., 141, 104422, 10.1016/j.autcon.2022.104422
U.S. Institute of Building Documentation
Meidow, 2009, Reasoning with uncertain points, str, aight lines, and straight line segments in 2D, ISPRS J. Photogramm. Remote Sens., 64, 125, 10.1016/j.isprsjprs.2008.09.013
Iwaszczuk, 2017, Camera pose refinement by matching uncertain 3D building models with thermal infrared image sequences for high quality texture extraction, ISPRS J. Photogramm. Remote Sens., 132, 33, 10.1016/j.isprsjprs.2017.08.006
Jarząbek-Rychard, 2022, Uncertainty modeling for point cloud-based automatic indoor scene reconstruction by strict error propagation analysis, 43
J.C., 1998
Krantz, 2003
Jarząbek-Rychard, 2016, 3D building reconstruction from ALS data using unambiguous decomposition into elementary structures, ISPRS J. Photogramm. Remote Sens., 118, 1, 10.1016/j.isprsjprs.2016.04.005