Laser scanning (LS) is a powerful tool for acquiring geometric information about structures. However, they face challenges in capturing targets and computing edges. This paper presents a method for automatically identifying point cloud (PC) planes and obtaining geometric information, which can provide construction quality. The proposed method consists of two parts. First, an iterative growth algorithm is introduced to identify multiple planes simultaneously with fewer parameters, which improves upon the traditional plane detection method that needs better robustness due to the abundance of parameters. Second, a framework is introduced for computing geometric information of PC planes, which integrates a k-dimensional tree to replace local density in the density peak clustering algorithm and computes edges while considering mixed points, significantly reducing the time and increasing precision. The effectiveness and accuracy of the method are verified by structural geometric information in the construction process.
BeslPJMcKayND (1991) A method for registration of 3-D shapes. In: Paper Presented at the Sensor Fusion Conf: Control Paradigms and Data Structures, SPIE.
2.
ChenZChenJShenF, et al. (2015) Collaborative mobile-cloud computing for civil infrastructure condition inspection. Journal of Computing in Civil Engineering29(5): 04014066.
3.
ChenJKiraZChoYK (2019) Deep learning approach to point cloud scene understanding for automated scan to 3D reconstruction. Journal of Computing in Civil Engineering33(4): 04019027.1–04019027.10.
4.
CzerniawskiTSankaranBNahangiM, et al. (2018) 6D DBSCAN-based segmentation of building point clouds for planar object classification. Automation in Construction88: 44–58.
5.
FilinSPfeiferN (2006) Segmentation of airborne laser scanning data using a slope adaptive neighborhood. ISPRS Journal of Photogrammetry and Remote Sensing60(2): 71–80.
6.
FotsingCNziengamNBobdaC (2020) Large common plansets-4-points congruent sets for point cloud registration. ISPRS International Journal of Geo-Information9(11): 647.
7.
FriasEPintoJSousaR, et al.(2022) Exploiting BIM objects for synthetic data generation toward indoor point cloud classification using deep learning. Journal of Computing in Civil Engineering36(6): Article 4022032.
8.
GillardJ (2011) Circular and linear regression: fitting circles and lines by least squares. Journal of the Royal Statistical Society - Series A: Statistics in Society174: 843.
9.
GrilliEMennaFRemondinoF (2017) A review of point clouds segmentation and classification algorithms. In: Paper presented at the conference on 3D virtual reconstruction and visualization of complex architectures, ISPRS.
10.
HuangJLZhuQSYangLJ, et al. (2016) A non-parameter outlier detection algorithm based on Natural Neighbor. Knowledge-Based Systems92: 71–77.
11.
HuangMWeiPCLiuXL (2019) An efficient encoding voxel-based segmentation (EVBS) algorithm based on fast adjacent voxel search for point cloud plane segmentation. Remote Sensing11(23): 2727.
12.
JaureguiDVWhiteKRWoodwardCB, et al. (2003) Noncontact photogrammetric measurement of vertical bridge deflection. Journal of Bridge Engineering8(4): 212–222.
13.
JiangSZhangJ (2019) Real‐time crack assessment using deep neural networks with wall‐climbing unmanned aerial system. Computer-Aided Civil and Infrastructure Engineering35(6): 549–564.
14.
KangCCWangWJKangCH (2012) Image segmentation with complicated background by using seeded region growing. AEU-International Journal of Electronics and Communications66(9): 767–771.
15.
KimCSonHKimC (2013) Automated construction progress measurement using a 4D building information model and 3D data. Automation in Construction31: 75–82.
16.
KimMKWangQParkJW, et al. (2016) Automated dimensional quality assurance of full-scale precast concrete elements using laser scanning and BIM. Automation in Construction72: 102–114.
17.
LiLYangFZhuHH, et al. (2017) An improved RANSAC for 3D point cloud plane segmentation based on normal distribution transformation cells. Remote Sensing9(5): 433.
18.
LiuRWangHYuXM (2018) Shared-nearest-neighbor-based clustering by fast search and find of density peaks. Information Sciences450: 200–226.
19.
MaalekRLichtiDDWalkerR, et al. (2019) Extraction of pipes and flanges from point clouds for automated verification of pre-fabricated modules in oil and gas refinery projects. Automation in Construction103: 150–167.
20.
OlsenMJKuesterFChangBJ, et al. (2010) Terrestrial laser scanning-based structural damage assessment. Journal of Computing in Civil Engineering24(3): 264–272.
21.
ParkHSLeeHMAdeliH, et al. (2007) A new approach for health monitoring of structures: terrestrial laser scanning. Computer-Aided Civil and Infrastructure Engineering22(1): 19–30.
22.
QiCRSuHMoKC, et al. (2017) PointNet: deep learning on point sets for 3D classification and segmentation. In: Paper presented at the 30th IEEE/CVF conference on computer vision and pattern recognition (CVPR). IEEE.
23.
RaguramRFrahmJMPollefeysM (2008) A comparative analysis of RANSAC techniques leading to adaptive real-time random sample consensus. In: Paper presented at the 10th European conference on computer vision (ECCV 2008). Springer.
24.
RashidiABrilakisIVelaP (2015) Generating absolute-scale point cloud data of built infrastructure scenes using a monocular camera setting. Journal of Computing in Civil Engineering29(6).
25.
RodriguezALaioA (2014) Machine learning. Clustering by fast search and find of density peaks. Science344(6191): 1492–1496.
26.
RusuRBMartonZCBlodowN, et al. (2008) Towards 3D point cloud based object maps for household environments. Robotics and Autonomous Systems56(11): 927–941.
TangPBHuberDAkinciB, et al. (2010) Automatic reconstruction of as-built building information models from laser-scanned point clouds: a review of related techniques. Automation in Construction19(7): 829–843.
29.
TeboulOSimonLKoutsourakisP, et al. (2010) Segmentation of building facades using procedural shape priors. In: Paper Presented at the 23rd IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE.
30.
TianYDZhangCJiangS, et al. (2021) Noncontact cable force estimation with unmanned aerial vehicle and computer vision. Computer-Aided Civil and Infrastructure Engineering36(1): 73–88.
31.
TommaselliAMGReissMLL (2005) A photogrammetric method for single image orientation and measurement. Photogrammetric Engineering & Remote Sensing71(6): 727–732.
32.
TurkanYBoscheFHaasCT, et al. (2012) Automated progress tracking using 4D schedule and 3D sensing technologies. Automation in Construction22: 414–421.
33.
VoAVTruong-HongLLaeferDF, et al. (2015) Octree-based region growing for point cloud segmentation. ISPRS Journal of Photogrammetry and Remote Sensing104: 88–100.
34.
WangQKimMKChengJCP, et al. (2016) Automated quality assessment of precast concrete elements with geometry irregularities using terrestrial laser scanning. Automation in Construction68: 170–182.
35.
WangQChengJCPSohnH (2017) Automated estimation of reinforced precast concrete rebar positions using colored laser scan data. Computer-Aided Civil and Infrastructure Engineering32(9): 787–802.
36.
WangQSohnHChengJCP (2019) Development of high-accuracy edge line estimation algorithms using terrestrial laser scanning. Automation in Construction101: 59–71.
37.
WidyaningrumEPetersRYLindenberghRC (2020) Building outline extraction from ALS point clouds using medial axis transform descriptors. Pattern Recognition106: 107447.
38.
XiongXHAdanAAkinciB, et al. (2013) Automatic creation of semantically rich 3D building models from laser scanner data. Automation in Construction31: 325–337.
39.
XuXDingSFDuMJ, et al. (2018a) DPCG: an efficient density peaks clustering algorithm based on grid. International Journal of Machine Learning and Cybernetics9(5): 743–754.
40.
XuYSYaoWTuttasS, et al. (2018b) Unsupervised segmentation of point clouds from buildings using hierarchical clustering based on gestalt principles. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing11(11): 4270–4286.
41.
ZhaoWJiangYLiuY, et al. (2022) Automated recognition and measurement based on three-dimensional point clouds to connect precast concrete components. Automation in Construction133: 104000.
