Abstract
The large-scale point cloud data representing buildings should be prudently pre-processed and down-sampled prior to post-data processing activities, such as data segmentation or classification and indoor scene analysis, and visualization of the processed results as three-dimensional (3D) digital models. However, current pre-processing tasks and down-sampling procedures encountered the challenge of preserving the geometric and semantic features of the scanned object and contributing to accurate 3D reconstruction due to the loss of significant features on buildings. Therefore, this paper proposed a framework that pre-processes unstructured laser scanning data to optimize the efficiency of data processing activities and the accuracy of the produced output, i.e., reconstructed 3D building models. The pre-processing framework includes building the topology of the unstructured dataset, down-sampling the raw data through an improved voxel-based approach, and performing indoor scene analysis such as normal estimation, point alignment, and data classification. The experimental results verified that the proposed framework efficiently pre-processes the input data and preserves the geometric characteristics of the digital models with high accuracy, thereby supporting reliable 3D reconstruction for various applications.
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References
Al-Durgham M (2014) The registration and segmentation of heterogeneous laser scanning data. PhD Thesis, University of Toronto, Toronto, Canada
Al-Rawabdeh A, He F, Habib A (2020) Automated feature-based down-sampling approaches for fine registration of irregular point clouds, Remote Sensing 12:1224, DOI: https://doi.org/10.3390/rs12071224
Blaszczak-Bak W, Janicka J, Suchocki C, Masiero A, Sobieraj-Zlobinska A (2020) Down-sampling of large LiDAR dataset in the context of off-road objects extraction. Geosciences 10:219, DOI: https://doi.org/10.3390/geosciences10060219
Chen B, Xiong C, Li W, He J, Zhang X (2021) Assessing surface texture features of asphalt pavement based on three-dimensional laser scanning technology. Buildings 11:623, DOI: https://doi.org/10.3390/buildings11120623
Choi Y, Park S, Kim S (2022) Development of point cloud data-denoising technology for earthwork sites using encoder-decoder network. KSCE Journal of Civil Engineering 26(11):4380–4389, DOI: https://doi.org/10.1007/s12205-022-0407-8
Cong B, Li Q, Liu R Wang F, Zhu D, Yang J (2022) Research on a point cloud registration method of mobile laser scanning and terrestrial laser scanning. KSCE Journal of Civil Engineering 26(11):5275–5290, DOI: https://doi.org/10.1007/s12205-022-0366-0
Ding Z, Liu S, Liao L, Zhang L (2019) A digital construction framework integrating building information modeling and reverse engineering technologies for renovation projects. Automation in Construction 102:45–58, DOI: https://doi.org/10.1016/j.autcon.2019.02.012
Du S, Zhang Y, Zou Z, Xu S, He X, Chen S (2017) Automatic building extraction from LiDAR data fusion of point and grid-based features. ISPRS Journal of Photogrammetry and Remote Sensing 130:294–307, DOI: https://doi.org/10.1016/j.isprsjprs.2017.06.005
Eckart B, Kim K, Troccoli A, Kelly A, Kautz J (2016) Accelerated generative models for 3D point cloud data. IEEE Conference on Computer Vision and Pattern Recognition. June 27–30, Las Vegas, NV, USA, DOI: https://doi.org/10.1109/CVPR.2016.593
El-Sayed E, Abdel-Kader R Nashaat H, Marei M (2018) Plane detection in 3D point cloud using octree-balanced density down-sampling and iterative adaptive plane extraction. IET Image Processing 12:1595–1605, DOI: https://doi.org/10.1049/iet-ipr.2017.1076
Fan L, Atkinson PM (2019) An iterative coarse-to-fine sub-sampling method for density reduction of terrain point clouds. Remote Sensing 11:947, DOI: https://doi.org/10.3390/rs11080947
Gargoum SA, Karsten L, El-Basyouny K, Koch JC (2018) Automated assessment of vertical clearance on highways scanned using mobile LiDAR technology. Automation in Construction 95:260–274, DOI: https://doi.org/10.1016/j.autcon.2018.08.015
Garrote L, Rosa J, Paulo J, Premebida C, Peixoto P, Nunes U (2017) 3D point cloud downsampling for 2D indoor scene modelling in mobile robotics. IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), April 26–28, Coimbra, Portugal, DOI: https://doi.org/10.1109/ICARSC.2017.7964080
Girardeau-Montaut D (2020) Cloudcompare - Open source project. Retrieved March 25, 2022, https://www.danielgm.net/cc/
Han XF, Jin JS, Wang MJ, Jiang W, Gao L, Xiao L (2017) A review of algorithms for filtering the 3D point cloud. Signal Processing: Image Communication 57:103–112, DOI: https://doi.org/10.1016/j.image.2017.05.009
He Y, Liang B, Yang J, Shunzhi L, He J (2017a) An iterative closest points algorithm for registration of 3D laser scanner point clouds with geometric features. Sensors 17:1862, DOI: https://doi.org/10.3390/s17081862
He F, Lin Y, Xiong W, Habib A (2017b) Alternative approaches for feature-based down-sampling of irregular point clouds for fine registration. The 10th International Symposium on Mobile Mapping Technology, May 6–8, Cairo, Egypt
Hong S, Jung J, Kim S, Cho H, Lee J, Heo J (2015) Semi-automated approach to indoor mapping for 3D as-built building information modeling. Computers, Environment and Urban Systems 51:34–43, DOI: https://doi.org/10.1016/j.compenvurbsys.2015.01.005
Ji A, Chew AWZ, Xue X, Zhang L (2022) An encoder-decoder deep learning method for multi-class object segmentation from 3D tunnel point clouds. Automation in Construction 137:104187, DOI: https://doi.org/10.1016/j.autcon.2022.104187
Kim M, Lee D, Kim T, Oh S, Cho H (2023) Automated extraction of geometric primitives with solid lines from unstructured point clouds for creating digital buildings models. Automation in Construction 145:104642, DOI: https://doi.org/10.1016/j.autcon.2022.104642
Lari Z, Habib A (2014) An adaptive approach for the segmentation and extraction of planar and linear/cylindrical features from laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing 93:192–212, DOI: https://doi.org/10.1016/j.isprsjprs.2013.12.001
Lee M, Lee S, Kwon S, Chin S (2017) A study on scan data matching for reverse engineering of pipes in plant construction. KSCE Journal of Civil Engineering 21:2027–2036, DOI: https://doi.org/10.1007/s12205-016-0929-z
Li Z, Ding Q Li R, Qin S (2014) A new extracting algorithm of k nearest neighbors searching for point clouds. Pattern Recognition Letters 49:162–170, DOI: https://doi.org/10.1016/j.patrec.2014.07.003
Lin Y, Benziger R Habib A (2016) Planar-based adaptive down-sampling of point clouds. Photogrammetric Engineering & Remote Sensing 82:955–966, DOI: https://doi.org/10.14358/PERS.82.12.955
Lin Y, Liu T, Zhang Y, Liu S, Ye L, Min H (2023) LA-Net: LSTM and attention based point cloud down-sampling and its application. Measurement and Control 56(7–8):1261–1277, DOI: https://doi.org/10.1177/00202940221149074
Liu K, Chen J, Xing S, Han H (2013) Simplification of point cloud data based on Gaussian curvature. IET International Conference on Smart and Sustainable City (ICSSC), August 19–20, Shanghai, China, DOI: https://doi.org/10.1049/cp.2013.1968
Nezhadarya E, Taghavi E, Razani R, Liu B, Luo J (2020) Adaptive hierarchical down-sampling for point cloud classification. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) June 13–19, Seattle, United States
Ochmann S, Vock R, Wessel R, Klein R (2016) Automated reconstruction of parametric building models from indoor point clouds. Computers & Graphics 54:94–103, DOI: https://doi.org/10.1016/j.cag.2015.07.008
Orts-Escolano S, Morell V, Garcia-Rodriguez J, Cazorla M (2013) Point cloud data filtering and downsampling using growing neural gas. The 2013 International Joint Conference on Neural Networks (IJCNN), August 4–9, Dallas, TX, USA, DOI: https://doi.org/10.1109/IJCNN.2013.6706719
Park Y, Guldmann JM (2019) Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach. Computers, Environment and Urban Systems 75:76–89, DOI: https://doi.org/10.1016/j.compenvurbsys.2019.01.004
Puttonen E, Lehtomaki M, Kaartinen H, Zhu L, Kukko A, Jaakkola A (2013) Improved sampling for terrestrial and mobile laser scanner point cloud data. Remote Sensing 5:1754–1773, DOI: https://doi.org/10.3390/rs5041754
Qiu Q, Wang M, Guo J, Liu Z, Wang Q (2022) An adaptive down-sampling method of laser scan data for scan-to-BIM. Automation in Construction 135:104135, DOI: https://doi.org/10.1016/j.autcon.2022.104135
Rebolj D, Pučko Z, Babič NČ, Bizjak M, Mongus D (2017) Point cloud quality requirements for Scan-vs-BIM based automated construction progress monitoring. Automation in Construction 84:323–334, DOI: https://doi.org/10.1016/j.autcon.2017.09.021
Ryu MW, Oh SM, Kim MJ, Cho HH, Son CB, Kim TH (2020) Algorithm for generating 3D geometric representation based on indoor point cloud data. Applied Sciences 10:8073, DOI: https://doi.org/10.3390/app10228073
Sanchez J, Denis F, Coeurjolly D, Dupont F, Trassoudaine L, Checchin P (2020) Robust normal vector estimation in 3D point clouds through iterative principal component analysis. ISPRS Journal of Photogrammetry and Remote Sensing 163:18–35, DOI: https://doi.org/10.1016/j.isprsjprs.2020.02.018
Suchocki C, Blaszczak-Bak W, Janicka J, Dumalski A (2020) Detection of defects in building walls using modified OptD method for down-sampling of point clouds. Building Research & Information 49(2):1–19, DOI: https://doi.org/10.1080/09613218.2020.1729687
Vidal J, Lin CY, Llado X, Marti R (2018) A method for 6D pose estimation of free-form rigid objects using point pair features on range data. Sensors 18:2678, DOI: https://doi.org/10.3390/s18082678
Vo AV, Truong-Hong L, Laefer DF, Bertolotto M (2015) Octree-based region growing for point cloud segmentation. ISPRS Journal of Photogrammetry and Remote Sensing 104:88–100, DOI: https://doi.org/10.1016/j.isprsjprs.2015.01.011
Wang D, Shu H (2022) Accuracy analysis of three-dimensional modeling of a multi-level UAV without control points. Buildings 12:592, DOI: https://doi.org/10.3390/buildings12050592
Wang J, Zhang S, Teizer J (2015) Geotechnical and safety protective equipment planning using range point data and rule checking in building information modeling. Automation in Construction 49:250–261, DOI: https://doi.org/10.1016/j.autcon.2014.09.002
Ximin Z, Xiaoging Y, Wanggen W, Junxing M, Qingmin L, Libing L (2013) The simplification of 3d color point cloud based on voxel. IET International Conference on Smart and Sustainable City (ICSSC), August 19–20, Shanghai, China, DOI: https://doi.org/10.1049/cp.2013.2032
Xu Y, Tong X, Stilla U (2021) Voxel-based representation of 3D point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126:103675, DOI: https://doi.org/10.1016/j.autcon.2021.103675
Yang F, Cao Y, Zhang W (2022) PSL-SLAM: A monocular SLAM system using points and structure lines in Manhattan World. International Journal of Intelligent Robotics and Applications 6:52–68, DOI: https://doi.org/10.1007/s41315-021-00204-0
Yang Y, Li H, Yang J, Zhong D (2019) Structured down-sampling and registration method for 3D point cloud of indoor scene. International Conference on Systems, Man and Cybernetics (SMC) October 6–9, Bari, Italy
Yin C, Cheng JC, Wang B, Gan VJ (2022) Automated classification of piping components from 3D LiDAR point clouds using SE-PseudoGrid. Automation in Construction 139:104300, DOI: https://doi.org/10.1016/j.autcon.2022.104300
Yin C, Yang B, Cheng JC, Gan VJ, Wang B, Yang J (2023) Label-efficient semantic segmentation of large-scale industrial point clouds using weakly supervised learning. Automation in Construction 148:104757, DOI: https://doi.org/10.1016/j.autcon.2023.104757
Zhang K, Qian S, Wang X, Yang Y, Zhang Y (2019) Feature-preserved point cloud simplification based on natural quadric shape models. Applied Sciences 9:2130, DOI: https://doi.org/10.3390/app9102130
Zhang Y, Liu K, Bao H, Zheng Y, Yang Y (2023) PMPF: Point-cloud multiple-pixel fusion-based 3D object detection for autonomous driving. Remote Sensing 15(6):1580, DOI: https://doi.org/10.3390/rs15061580
Acknowledgments
This research was supported by a grant (RS-2022-00143493, project number:1615012983) from Digital-Based Building Construction and Safety Supervision Technology Research Program funded by Ministry of Land, Infrastructure and Transport of Korean Government. Also, this research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2021R1A5A1032433).
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Kim, M., Kim, H. Optimal Pre-processing of Laser Scanning Data for Indoor Scene Analysis and 3D Reconstruction of Building Models. KSCE J Civ Eng 28, 1–14 (2024). https://doi.org/10.1007/s12205-023-2406-9
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DOI: https://doi.org/10.1007/s12205-023-2406-9