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Computational Methods of Acquisition and Processing of 3D Point Cloud Data for Construction Applications

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Abstract

3D point cloud data from sensing technologies such as 3D laser scanning and photogrammetry are able to capture the 3D surface geometries of target objects in an accurate and efficient manner. Due to these advantages, the construction industry has been capturing 3D point cloud data of construction sites, construction works, and construction equipment to enable better decision making in construction project management. The captured point cloud data are utilized to reconstruct 3D building models, check construction quality, monitor construction progress, improve construction safety etc. throughout the project lifecycle from design to construction and facilities management phase. This paper aims to review the state-of-the-art methods to acquire and process 3D point cloud data for construction applications. The different approaches to 3D point cloud data acquisition are reviewed and compared including 3D laser scanning, photogrammetry, videogrammetry, RGB-D camera, and stereo camera. Furthermore, the processing methods of 3D point cloud data are reviewed according to the four common processing procedures including (1) data cleansing, (2) data registration, (3) data segmentation, and (4) object recognition. For each processing procedure, the different processing methods and algorithms are compared and discussed in detail, which provides a useful guidance to both researchers and industry practitioners for adopting point cloud data in the construction industry.

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References

  1. Bradley C, Vickers G, Milroy M (1994) Reverse engineering of quadric surfaces employing three-dimensional laser scanning. Proc Inst Mech Eng Part B J Eng Manuf 208(1):21–28

    Google Scholar 

  2. Son S, Park H, Lee KH (2002) Automated laser scanning system for reverse engineering and inspection. Int J Mach Tools Manuf 42(8):889–897

    Google Scholar 

  3. Varady T, Martin RR, Cox J (1997) Reverse engineering of geometric models—an introduction. Comput Aided Des 29(4):255–268

    Google Scholar 

  4. Yu X, Hyyppä J, Kaartinen H, Maltamo M (2004) Automatic detection of harvested trees and determination of forest growth using airborne laser scanning. Remote Sens Environ 90(4):451–462

    Google Scholar 

  5. Gaveau DL, Hill RA (2003) Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning data. Can J Remote Sens 29(5):650–657

    Google Scholar 

  6. Hollaus M, Wagner W, Maier B, Schadauer K (2007) Airborne laser scanning of forest stem volume in a mountainous environment. Sensors 7(8):1559–1577

    Google Scholar 

  7. Rosser N, Petley D, Lim M, Dunning S, Allison R (2005) Terrestrial laser scanning for monitoring the process of hard rock coastal cliff erosion. Q J Eng Geol Hydrogeol 38(4):363–375

    Google Scholar 

  8. Heritage GL, Milan DJ (2009) Terrestrial laser scanning of grain roughness in a gravel-bed river. Geomorphology 113(1–2):4–11

    Google Scholar 

  9. Liu X (2008) Airborne LiDAR for DEM generation: some critical issues. Prog Phys Geogr 32(1):31–49

    Google Scholar 

  10. Fröhlich C, Mettenleiter M (2004) Terrestrial laser scanning—new perspectives in 3D surveying. Int Arch Photogramm Remote Sens Spat Inf Sci 36(Part 8):W2

    Google Scholar 

  11. Olsen MJ, Kuester F, Chang BJ, Hutchinson TC (2009) Terrestrial laser scanning-based structural damage assessment. J Comput Civ Eng 24(3):264–272

    Google Scholar 

  12. Zhang C, Arditi D (2013) Automated progress control using laser scanning technology. Autom Constr 36:108–116

    Google Scholar 

  13. Leite F, Cho Y, Behzadan AH, Lee S, Choe S, Fang Y, Akhavian R, Hwang S (2016) Visualization, information modeling, and simulation: grand challenges in the construction industry. J Comput Civ Eng 30(6):04016035

    Google Scholar 

  14. Kwon S, Lee M, Lee M, Lee S, Lee J (2013) Development of optimized point cloud merging algorithms for accurate processing to create earthwork site models. Autom Constr 35:618–624

    MathSciNet  Google Scholar 

  15. Bosche F, Haas CT, Akinci B (2009) Automated recognition of 3D CAD objects in site laser scans for project 3D status visualization and performance control. J Comput Civ Eng 23(6):311–318

    Google Scholar 

  16. Wang C, Cho YK (2015) Smart scanning and near real-time 3D surface modeling of dynamic construction equipment from a point cloud. Autom Constr 49:239–249

    Google Scholar 

  17. Tang P, Huber D, Akinci B, Lipman R, Lytle A (2010) Automatic reconstruction of as-built building information models from laser-scanned point clouds: a review of related techniques. Autom Constr 19(7):829–843

    Google Scholar 

  18. Wang Q, Kim M-K, Cheng JC, Sohn H (2016) Automated quality assessment of precast concrete elements with geometry irregularities using terrestrial laser scanning. Autom Constr 68:170–182

    Google Scholar 

  19. Volk R, Stengel J, Schultmann F (2014) Building information modeling (BIM) for existing buildings—literature review and future needs. Autom Constr 38:109–127

    Google Scholar 

  20. Kim M-K, Cheng JC, Sohn H, Chang C-C (2015) A framework for dimensional and surface quality assessment of precast concrete elements using BIM and 3D laser scanning. Autom Constr 49:225–238

    Google Scholar 

  21. Kim C, Son H, Kim C (2013) Automated construction progress measurement using a 4D building information model and 3D data. Autom Constr 31:75–82. https://doi.org/10.1016/j.autcon.2012.11.041

    Article  Google Scholar 

  22. Wang J, Zhang S, Teizer J (2015) Geotechnical and safety protective equipment planning using range point cloud data and rule checking in building information modeling. Autom Constr 49:250–261

    Google Scholar 

  23. Chae MJ, Lee GW, Kim JY, Park JW, Cho MY (2011) A 3D surface modeling system for intelligent excavation system. Autom Constr 20(7):808–817

    Google Scholar 

  24. Xu J, Ding L, Love PE (2017) Digital reproduction of historical building ornamental components: from 3D scanning to 3D printing. Autom Constr 76:85–96

    Google Scholar 

  25. Aydin CC (2014) Designing building façades for the urban rebuilt environment with integration of digital close-range photogrammetry and geographical information systems. Autom Constr 43:38–48

    Google Scholar 

  26. Balado J, Díaz-Vilariño L, Arias P, Soilán M (2017) Automatic building accessibility diagnosis from point clouds. Autom Constr 82:103–111

    Google Scholar 

  27. Karan EP, Sivakumar R, Irizarry J, Guhathakurta S (2013) Digital modeling of construction site terrain using remotely sensed data and geographic information systems analyses. J Constr Eng Manag 140(3):04013067

    Google Scholar 

  28. Kim M-K, Sohn H, Chang C-C (2014) Automated dimensional quality assessment of precast concrete panels using terrestrial laser scanning. Autom Constr 45:163–177

    Google Scholar 

  29. Wang Q, Cheng JC, Sohn H (2017) Automated estimation of reinforced precast concrete rebar positions using colored laser scan data. Comput Aided Civ Infrastruct Eng 32(9):787–802

    Google Scholar 

  30. Wang Q, Kim M-K, Sohn H, Cheng JC (2016) Surface flatness and distortion inspection of precast concrete elements using laser scanning technology. Smart Struct Syst 18(3):601–623

    Google Scholar 

  31. Turkan Y, Bosche F, Haas CT, Haas R (2012) Automated progress tracking using 4D schedule and 3D sensing technologies. Autom Constr 22:414–421

    Google Scholar 

  32. El-Omari S, Moselhi O (2008) Integrating 3D laser scanning and photogrammetry for progress measurement of construction work. Autom Constr 18(1):1–9

    Google Scholar 

  33. Marks ED, Cheng T, Teizer J (2013) Laser scanning for safe equipment design that increases operator visibility by measuring blind spots. J Constr Eng Manag 139(8):1006–1014

    Google Scholar 

  34. Bosché F, Guenet E (2014) Automating surface flatness control using terrestrial laser scanning and building information models. Autom Constr 44:212–226

    Google Scholar 

  35. Erkal BG, Hajjar JF (2017) Laser-based surface damage detection and quantification using predicted surface properties. Autom Constr 83:285–302

    Google Scholar 

  36. Kashani AG, Crawford PS, Biswas SK, Graettinger AJ, Grau D (2014) Automated tornado damage assessment and wind speed estimation based on terrestrial laser scanning. J Comput Civ Eng 29(3):04014051

    Google Scholar 

  37. Riveiro B, Lourenço PB, Oliveira DV, González-Jorge H, Arias P (2016) Automatic morphologic analysis of quasi-periodic masonry walls from LiDAR. Comput Aided Civ Infrastruct Eng 31(4):305–319

    Google Scholar 

  38. Wang C, Cho YK, Gai M (2012) As-is 3D thermal modeling for existing building envelopes using a hybrid LIDAR system. J Comput Civ Eng 27(6):645–656

    Google Scholar 

  39. Zalama E, Gómez-García-Bermejo J, Llamas J, Medina R (2011) An effective texture mapping approach for 3D models obtained from laser scanner data to building documentation. Comput Aided Civ Infrastruct Eng 26(5):381–392

    Google Scholar 

  40. Olsen MJ (2013) In situ change analysis and monitoring through terrestrial laser scanning. J Comput Civ Eng 29(2):04014040

    Google Scholar 

  41. Hansen EH, Gobakken T, Næsset E (2015) Effects of pulse density on digital terrain models and canopy metrics using airborne laser scanning in a tropical rainforest. Remote Sensing 7(7):8453–8468

    Google Scholar 

  42. Xiao W, Vallet B, Schindler K, Paparoditis N (2016) Street-side vehicle detection, classification and change detection using mobile laser scanning data. ISPRS J Photogramm Remote Sens 114:166–178

    Google Scholar 

  43. Yang B, Fang L, Li J (2013) Semi-automated extraction and delineation of 3D roads of street scene from mobile laser scanning point clouds. ISPRS J Photogramm Remote Sens 79:80–93

    Google Scholar 

  44. Li J, Bao H, Han X, Pan F, Pan W, Zhang F, Wang D (2017) Real-time self-driving car navigation and obstacle avoidance using mobile 3D laser scanner and GNSS. Multimed Tools Appl 76(21):23017–23039

    Google Scholar 

  45. Basaca-Preciado LC, Sergiyenko OY, Rodríguez-Quinonez JC, Garcia X, Tyrsa VV, Rivas-Lopez M, Hernandez-Balbuena D, Mercorelli P, Podrygalo M, Gurko A (2014) Optical 3D laser measurement system for navigation of autonomous mobile robot. Opt Lasers Eng 54:159–169

    Google Scholar 

  46. RIEGL (2018) RIEGL laser measurement systems. vol 2018

  47. ASPRS (2017) Definition of photogrammetry. vol 2017

  48. Lu Q, Lee S (2017) Image-Based technologies for constructing as-is building information models for existing buildings. J Comput Civ Eng 31(4):04017005

    Google Scholar 

  49. Brilakis I, Fathi H, Rashidi A (2011) Progressive 3D reconstruction of infrastructure with videogrammetry. Autom Constr 20(7):884–895

    Google Scholar 

  50. Bellés C (2015) A kinect-based system for 3D reconstruction of sewer manholes. Comput Aided Civ Infrastruct Eng 30(11):906–917

    Google Scholar 

  51. Xiao Y, Feng C, Taguchi Y, Kamat VR (2017) User-guided dimensional analysis of indoor building environments from single frames of RGB-D sensors. J Comput Civ Eng 31(4):04017006

    Google Scholar 

  52. Henry P, Krainin M, Herbst E, Ren X, Fox D (2012) RGB-D mapping: using kinect-style depth cameras for dense 3D modeling of indoor environments. Int J Robot Res 31(5):647–663

    Google Scholar 

  53. Rebolj D, Pučko Z, Babič NČ, Bizjak M, Mongus D (2017) Point cloud quality requirements for Scan-vs-BIM based automated construction progress monitoring. Autom Constr 84:323–334

    Google Scholar 

  54. Son H, Kim C (2010) 3D structural component recognition and modeling method using color and 3D data for construction progress monitoring. Autom Constr 19(7):844–854

    Google Scholar 

  55. Autodesk (2018) Autodesk® ReCap™. vol 2018

  56. Agisoft (2018) Agisoft PhotoScan. vol 2018

  57. Trimble (2018) Trimble laser scanner. vol 2018

  58. FARO (2018) FARO Focus 3D terrestrial laser scanner. vol 2018

  59. Leica (2018) Leica ScanStation. vol 2018

  60. RIEGL (2018) RIEGL—terrestrial scanning. vol 2018

  61. Bosché F, Ahmed M, Turkan Y, Haas CT, Haas R (2015) The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: the case of cylindrical MEP components. Autom Constr 49:201–213

    Google Scholar 

  62. Kim M-K, Sohn H, Chang C-C (2014) Localization and quantification of concrete spalling defects using terrestrial laser scanning. J Comput Civ Eng 29(6):04014086

    Google Scholar 

  63. Kim M-K, Wang Q, Park J-W, Cheng JC, Sohn H, Chang C-C (2016) Automated dimensional quality assurance of full-scale precast concrete elements using laser scanning and BIM. Autom Constr 72:102–114

    Google Scholar 

  64. Bosché F, Biotteau B (2015) Terrestrial laser scanning and continuous wavelet transform for controlling surface flatness in construction—a first investigation. Adv Eng Inform 29(3):591–601

    Google Scholar 

  65. Ahmed MF, Haas CT, Haas R (2014) Automatic detection of cylindrical objects in built facilities. J Comput Civ Eng 28(3):04014009

    Google Scholar 

  66. Pandžić J, Pejić M, Božić B, Erić V (2017) Error model of direct georeferencing procedure of terrestrial laser scanning. Autom Constr 78:13–23

    Google Scholar 

  67. Son H, Kim C, Kim C (2015) 3D reconstruction of as-built industrial instrumentation models from laser-scan data and a 3D CAD database based on prior knowledge. Autom Constr 49:193–200

    Google Scholar 

  68. Lee J, Son H, Kim C, Kim C (2013) Skeleton-based 3D reconstruction of as-built pipelines from laser-scan data. Autom Constr 35:199–207

    Google Scholar 

  69. Ma L, Sacks R, Zeibak-Shini R, Aryal A, Filin S (2015) Preparation of synthetic as-damaged models for post-earthquake BIM reconstruction research. J Comput Civ Eng 30(3):04015032

    Google Scholar 

  70. Son H, Kim C, Kim C (2014) Fully automated as-built 3D pipeline extraction method from laser-scanned data based on curvature computation. J Comput Civ Eng 29(4):B4014003

    MathSciNet  Google Scholar 

  71. Han S, Cho H, Kim S, Jung J, Heo J (2012) Automated and efficient method for extraction of tunnel cross sections using terrestrial laser scanned data. J Comput Civ Eng 27(3):274–281

    Google Scholar 

  72. Truong-Hong L, Laefer DF, Hinks T, Carr H (2011) Flying voxel method with Delaunay triangulation criterion for façade/feature detection for computation. J Comput Civ Eng 26(6):691–707

    Google Scholar 

  73. Truong-Hong L, Laefer DF, Hinks T, Carr H (2013) Combining an angle criterion with voxelization and the flying voxel method in reconstructing building models from LiDAR data. Comput Aided Civ Infrastruct Eng 28(2):112–129

    Google Scholar 

  74. Oskouie P, Becerik-Gerber B, Soibelman L (2016) Automated measurement of highway retaining wall displacements using terrestrial laser scanners. Autom Constr 65:86–101

    Google Scholar 

  75. Mizoguchi T, Koda Y, Iwaki I, Wakabayashi H, Kobayashi Y, Shirai K, Hara Y, Lee H-S (2013) Quantitative scaling evaluation of concrete structures based on terrestrial laser scanning. Autom Constr 35:263–274

    Google Scholar 

  76. Martínez J, Soria-Medina A, Arias P, Buffara-Antunes AF (2012) Automatic processing of terrestrial laser scanning data of building facades. Autom Constr 22:298–305

    Google Scholar 

  77. Valero E, Adán A, Bosché F (2015) Semantic 3D reconstruction of furnished interiors using laser scanning and RFID technology. J Comput Civ Eng 30(4):04015053

    Google Scholar 

  78. Bhatla A, Choe SY, Fierro O, Leite F (2012) Evaluation of accuracy of as-built 3D modeling from photos taken by handheld digital cameras. Autom Constr 28:116–127

    Google Scholar 

  79. Klein L, Li N, Becerik-Gerber B (2012) Imaged-based verification of as-built documentation of operational buildings. Autom Constr 21:161–171

    Google Scholar 

  80. Yilmaz H, Yakar M, Yildiz F (2008) Documentation of historical caravansaries by digital close range photogrammetry. Autom Constr 17(4):489–498

    Google Scholar 

  81. Riveiro B, Caamaño J, Arias P, Sanz E (2011) Photogrammetric 3D modelling and mechanical analysis of masonry arches: an approach based on a discontinuous model of voussoirs. Autom Constr 20(4):380–388

    Google Scholar 

  82. Riveiro B, Jauregui D, Arias P, Armesto J, Jiang R (2012) An innovative method for remote measurement of minimum vertical underclearance in routine bridge inspection. Autom Constr 25:34–40

    Google Scholar 

  83. Golparvar-Fard M, Bohn J, Teizer J, Savarese S, Peña-Mora F (2011) Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques. Autom Constr 20(8):1143–1155

    Google Scholar 

  84. Khaloo A, Lattanzi D (2016) Hierarchical dense structure-from-motion reconstructions for infrastructure condition assessment. J Comput Civ Eng 31(1):04016047

    Google Scholar 

  85. Styliadis AD (2007) Digital documentation of historical buildings with 3-d modeling functionality. Autom Constr 16(4):498–510

    Google Scholar 

  86. Rashidi A, Dai F, Brilakis I, Vela P (2013) Optimized selection of key frames for monocular videogrammetric surveying of civil infrastructure. Adv Eng Inform 27(2):270–282

    Google Scholar 

  87. Amazon (2018) Kinect for Windows. vol 2018

  88. Smisek J, Jancosek M, Pajdla T (2013) 3D with kinect, consumer depth cameras for computer vision. Springer, Berlin, pp 3–25

    Google Scholar 

  89. F.I.I. Solutions (2018) Bumblebee XB3 1394b. vol 2018

  90. Hebert M, Krotkov E (1992) 3D measurements from imaging laser radars: How good are they? Image Vis Comput 10(3):170–178

    Google Scholar 

  91. Adams MD, Probert PJ (1996) The interpretation of phase and intensity data from AMCW light detection sensors for reliable ranging. Int J Robot Res 15(5):441–458

    Google Scholar 

  92. Tang P, Huber D, Akinci B (2007) A comparative analysis of depth-discontinuity and mixed-pixel detection algorithms, 3-D digital imaging and modeling, 2007. In: 3DIM’07. Sixth international conference on, IEEE, pp 29–38

  93. Wang Q, Sohn H, Cheng JC (2016) Development of a mixed pixel filter for improved dimension estimation using AMCW laser scanner. ISPRS J Photogramm Remote Sens 119:246–258

    Google Scholar 

  94. Gong J, Caldas CH (2008) Data processing for real-time construction site spatial modeling. Autom Constr 17(5):526–535

    Google Scholar 

  95. Schall O, Belyaev A, Seidel H-P (2005) Robust filtering of noisy scattered point data. In: Point-based graphics, 2005. Eurographics/IEEE VGTC symposium proceedings, IEEE, pp 71–144

  96. Wang J, Xu K, Liu L, Cao J, Liu S, Yu Z, Gu XD (2013) Consolidation of low-quality point clouds from outdoor scenes, computer graphics forum, vol 32. Wiley, Hoboken, pp 207–216

    Google Scholar 

  97. Lange C, Polthier K (2005) Anisotropic smoothing of point sets. Comput Aided Geom Des 22(7):680–692

    MathSciNet  MATH  Google Scholar 

  98. Kanzok T, Süß F, Linsen L, Rosenthal P (2013) Efficient removal of inconsistencies in large multi-scan point clouds. In Proceedings of 21st International Conference on Computer Graphics, Visualization and Computer Vision, Plzen, Czech Republic

  99. Besl PJ, McKay ND (1992) Method for registration of 3-D shapes. In: Schenker PS (ed) sensor Fusion IV: control paradigms and data structures, vol 1611, international society for optics and photonics, pp 586–607

  100. Chen Y, Medioni G (1992) Object modelling by registration of multiple range images. Image Vis Comput 10(3):145–155

    Google Scholar 

  101. Bergevin R, Soucy M, Gagnon H, Laurendeau D (1996) Towards a general multi-view registration technique. IEEE Trans Pattern Anal Mach Intell 18(5):540–547

    Google Scholar 

  102. Bae K-H, Lichti DD (2006) Automated registration of unorganised point clouds from terrestrial laser scanners. Curtin University of Technology

  103. Minguez J, Montesano L, Lamiraux F (2006) Metric-based iterative closest point scan matching for sensor displacement estimation. IEEE Trans Rob 22(5):1047–1054

    Google Scholar 

  104. Censi A (2008) An ICP variant using a point-to-line metric. In: Robotics and automation, 2008. ICRA 2008. IEEE international conference on, IEEE, pp 19–25

  105. Weinmann M, Jutzi B, Mallet C (2014) Semantic 3D scene interpretation: a framework combining optimal neighborhood size selection with relevant features. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 2(3):181

    Google Scholar 

  106. Weinmann M, Schmidt A, Mallet C, Hinz S, Rottensteiner F, Jutzi B (2015) Contextual classification of point cloud data by exploiting individual 3D neigbourhoods. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 2(3):271

    Google Scholar 

  107. Weinmann M, Urban S, Hinz S, Jutzi B, Mallet C (2015) Distinctive 2D and 3D features for automated large-scale scene analysis in urban areas. Comput Graph 49:47–57. https://doi.org/10.1016/j.cag.2015.01.006

    Article  Google Scholar 

  108. Gelfand N, Mitra NJ, Guibas LJ, Pottmann H (2005) Robust global registration. In: Symposium on geometry processing, vol 2, p 5

  109. Rusu RB, Blodow N, Marton ZC, Beetz M (2008) Aligning point cloud views using persistent feature histograms. In: 2008 IEEE/RSJ international conference on intelligent robots and systems, pp 3384–3391

  110. Rusu RB, Blodow N, Beetz M (2009) Fast point feature histograms (FPFH) for 3D registration. In: 2009 IEEE international conference on robotics and automation, pp 3212–3217

  111. Salti S, Tombari F, Di Stefano L (2014) SHOT: unique signatures of histograms for surface and texture description. Comput Vis Image Underst 125:251–264

    Google Scholar 

  112. Rusu RB, Cousins S (2011) 3D is here: Point Cloud Library (PCL). In: 2011 IEEE international conference on robotics and automation, pp 1–4

  113. Theiler PW, Wegner JD, Schindler K (2014) Keypoint-based 4-points congruent sets—automated marker-less registration of laser scans. ISPRS J Photogramm Remote Sens 96:149–163. https://doi.org/10.1016/j.isprsjprs.2014.06.015

    Article  Google Scholar 

  114. Theiler PW, Wegner JD, Schindler K (2014) Fast registration of laser scans with 4-point congruent sets-what works and what doesn’t. ISPRS Ann Photogramm Remote Sens Spat Information Sci 2(3):149

    Google Scholar 

  115. Lowe DG (1999) Object recognition from local scale-invariant features. In: Computer vision, 1999. The proceedings of the seventh IEEE international conference on, vol 2, IEEE, pp 1150–1157

  116. Allaire S, Kim JJ, Breen SL, Jaffray DA, Pekar V (2008) Full orientation invariance and improved feature selectivity of 3D SIFT with application to medical image analysis. In: 2008 IEEE computer society conference on computer vision and pattern recognition workshops, pp 1–8

  117. Li X, Guskov I (2005) Multiscale features for approximate alignment of point-based surfaces. In: Symposium on geometry processing, vol 255, p 217

  118. Theiler P, Schindler K (2012) Automatic registration of terrestrial laser scanner point clouds using natural planar surfaces. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 3:173–178

    Google Scholar 

  119. Rublee E, Rabaud V, Konolige K, Bradski G (2011) ORB: an efficient alternative to SIFT or SURF. In: Computer vision (ICCV), 2011 IEEE international conference on, IEEE, pp 2564–2571

  120. Alcantarilla PF, Solutions T (2011) Fast explicit diffusion for accelerated features in nonlinear scale spaces. IEEE Trans Pattern Anal Mach Intell 34(7):1281–1298

    Google Scholar 

  121. Agrawal M, Konolige K, Blas MR (2008) Censure: center surround extremas for realtime feature detection and matching In: European conference on computer vision, Springer, pp 102–115

  122. Trzcinski T, Christoudias M, Lepetit V, Fua P (2012) Learning image descriptors with the boosting-trick. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (ed) Advances in neural information processing systems, pp 269–277

  123. Trzcinski T, Christoudias M, Fua P, Lepetit V (2013) Boosting binary keypoint descriptors. In: Computer vision and pattern recognition (CVPR), 2013 IEEE conference on, IEEE, pp 2874–2881

  124. Urban S, Weinmann M (2015) Finding a good feature detector-descriptor combination for the 2D keypoint-based registration of TLS point clouds. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 2:121–128

    Google Scholar 

  125. Aiger D, Mitra NJ, Cohen-Or D (2008) 4-points congruent sets for robust pairwise surface registration. In: ACM transactions on graphics (TOG), vol 27, ACM, p 85

  126. Theiler PW, Wegner JD, Schindler K (2013) Markerless point cloud registration with keypoint-based 4-points congruent sets. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 1(2):283–288

    Google Scholar 

  127. Torr PH, Zisserman A (2000) MLESAC: a new robust estimator with application to estimating image geometry. Comput Vis Image Underst 78(1):138–156

    Google Scholar 

  128. Mohamad M, Rappaport D, Greenspan M (2014) Generalized 4-points congruent sets for 3D registration. In: 2014 2nd international conference on 3D vision, vol 1, pp 83–90

  129. Mohamad M, Ahmed MT, Rappaport D, Greenspan M (2015) Super generalized 4pcs for 3d registration. In: 3D vision (3DV), 2015 international conference on, IEEE, pp 598–606

  130. Mellado N, Aiger D, Mitra NJ (2014) Super 4pcs fast global pointcloud registration via smart indexing. In: Computer graphics forum, vol 33, Wiley Online Library, pp 205–215

  131. Bueno M, González-Jorge H, Martínez-Sánchez J, Lorenzo H (2017) Automatic point cloud coarse registration using geometric keypoint descriptors for indoor scenes. Autom Constr 81:134–148

    Google Scholar 

  132. Nguyen A, Le B (2013) 3D point cloud segmentation: a survey. 2013 6th IEEE conference on robotics, automation and mechatronics (RAM). IEEE

  133. Biosca JM, Lerma JL (2008) Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods. ISPRS J Photogramm Remote Sens 63(1):84–98

    Google Scholar 

  134. Filin S (2002) Surface clustering from airborne laser scanning data. Int Arch Photogramm Remote Sens Spat Inf Sci 34(3/A):119–124

    Google Scholar 

  135. Filin S, Pfeifer N (2006) Segmentation of airborne laser scanning data using a slope adaptive neighborhood. ISPRS J Photogramm Remote Sens 60(2):71–80

    Google Scholar 

  136. Lu X, Yao J, Tu J, Li K, Li L, Liu Y (2016) Pairwise linkage for point cloud segmentation. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 3(3):201–208

    Google Scholar 

  137. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619

    Google Scholar 

  138. MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 1, Oakland, CA, USA, pp 281–297

  139. Yamauchi H, Lee S, Lee Y, Ohtake Y, Belyaev A, Seidel H-P (2005) Feature sensitive mesh segmentation with mean shift. In: Shape modeling and applications, 2005 international conference, IEEE, pp 236–243

  140. Zhang X, Li G, Xiong Y, He F (2008) 3D mesh segmentation using mean-shifted curvature. In: International conference on geometric modeling and processing, Springer, pp 465–474

  141. Bhanu B, Lee S, Ho C-C, Henderson T (1986) Range data processing: representation of surfaces by edges. In: Proceedings of the eighth international conference on pattern recognition, pp 236–238

  142. Castillo E, Liang J, Zhao H (2013) Point cloud segmentation and denoising via constrained nonlinear least squares normal estimates. In: Breuß M, Bruckstein A, Maragos P (ed) Innovations for shape analysis, Springer, Berlin, pp 283–299

    Google Scholar 

  143. Jiang XY, Meier U, Bunke H (1996) Fast range image segmentation using high-level segmentation primitives. In: Applications of computer vision, 1996. WACV’96., proceedings 3rd IEEE workshop on, IEEE, pp 83–88

  144. Sappa AD, Devy M (2001) Fast range image segmentation by an edge detection strategy. In: 3-D digital imaging and modeling, 2001. Proceedings. Third international conference on, IEEE, pp 292–299

  145. Wani MA, Arabnia HR (2003) Parallel edge-region-based segmentation algorithm targeted at reconfigurable multiring network. J Supercomput 25(1):43–62

    MATH  Google Scholar 

  146. Rabbani T, Van Den Heuvel F, Vosselmann G (2006) Segmentation of point clouds using smoothness constraint. Int Arch Photogramm Remote Sens Spat Inf Sci 36(5):248–253

    Google Scholar 

  147. Besl PJ, Jain RC (1988) Segmentation through variable-order surface fitting. IEEE Trans Pattern Anal Mach Intell 10(2):167–192

    Google Scholar 

  148. Chen J, Chen B (2008) Architectural modeling from sparsely scanned range data. Int J Comput Vision 78(2–3):223–236

    Google Scholar 

  149. Dorninger P, Nothegger C (2007) 3D segmentation of unstructured point clouds for building modelling. Int Arch Photogramm Remote Sens Spat Inf Sci 35(3/W49A):191–196

    Google Scholar 

  150. Ning X, Zhang X, Wang Y, Jaeger M (2009) Segmentation of architecture shape information from 3D point cloud. In: Proceedings of the 8th international conference on virtual reality continuum and its applications in industry, ACM, pp 127–132

  151. Pu S, Vosselman G (2006) Automatic extraction of building features from terrestrial laser scanning. Int Arch Photogramm Remote Sens Spat Inf Sci 36(5):25–27

    Google Scholar 

  152. Rusu RB, Marton ZC, Blodow N, Dolha M, Beetz M (2008) Towards 3D point cloud based object maps for household environments. Robot Auton Syst 56(11):927–941

    Google Scholar 

  153. Tóvári D, Pfeifer N (2005) Segmentation based robust interpolation-a new approach to laser data filtering. Int Arch Photogramm Remote Sens Spat Inf Sci 36(3/19):79–84

    Google Scholar 

  154. Vosselman G, Gorte BG, Sithole G, Rabbani T (2004) Recognising structure in laser scanner point clouds. Int Arch Photogramm Remote Sens Spat Inf Sci 46(8):33–38

    Google Scholar 

  155. Belton D, Lichti DD (2006) Classification and segmentation of terrestrial laser scanner point clouds using local variance information. Int Arch Photogramm Remote Sens Spat Inf Sci 36(5):44–49

    Google Scholar 

  156. Klasing K, Althoff D, Wollherr D, Buss M (2009) Comparison of surface normal estimation methods for range sensing applications. In: Robotics and automation, 2009. ICRA’09. IEEE international conference on, IEEE, pp 3206–3211

  157. Liu Y, Xiong Y (2008) Automatic segmentation of unorganized noisy point clouds based on the Gaussian map. Comput Aided Des 40(5):576–594

    MathSciNet  Google Scholar 

  158. Vo A-V, Truong-Hong L, Laefer DF, Bertolotto M (2015) Octree-based region growing for point cloud segmentation. ISPRS J Photogramm Remote Sens 104:88–100

    Google Scholar 

  159. Xiao J, Zhang J, Adler B, Zhang H, Zhang J (2013) Three-dimensional point cloud plane segmentation in both structured and unstructured environments. Robot Auton Syst 61(12):1641–1652

    Google Scholar 

  160. Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vision 59(2):167–181

    Google Scholar 

  161. Golovinskiy A, Funkhouser T (2009) Min-cut based segmentation of point clouds. In: Computer vision workshops (ICCV workshops), 2009 IEEE 12th international conference on, IEEE, pp 39–46

  162. Rusu RB, Holzbach A, Blodow N, Beetz M (2009) Fast geometric point labeling using conditional random fields. IROS, pp 7–12

  163. Schoenberg JR, Nathan A, Campbell M (2010) Segmentation of dense range information in complex urban scenes. In: Intelligent robots and systems (IROS), 2010 IEEE/RSJ international conference on, IEEE, pp 2033–2038

  164. Strom J, Richardson A, Olson E (2010) Graph-based segmentation for colored 3D laser point clouds. In: Intelligent robots and systems (IROS), 2010 IEEE/RSJ international conference on, IEEE, pp 2131–2136

  165. Vosselman G, Dijkman S (2001) 3D building model reconstruction from point clouds and ground plans. Int Arch Photogramm Remote Sens Spat Inf Sci 34(3/W4):37–44

    Google Scholar 

  166. Ballard DH (1981) Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn 13(2):111–122

    MATH  Google Scholar 

  167. Maas H-G, Vosselman G (1999) Two algorithms for extracting building models from raw laser altimetry data. ISPRS J Photogramm Remote Sens 54(2–3):153–163

    Google Scholar 

  168. Overby J, Bodum L, Kjems E, Iisoe P (2004) Automatic 3D building reconstruction from airborne laser scanning and cadastral data using Hough transform. Int Arch Photogramm Remote Sens Spat Inf Sci 34(Part B3):296–301

    Google Scholar 

  169. Bretar F, Roux M (2005) Extraction of 3D planar primitives from raw airborne laser data: a normal driven RANSAC approach, MVA, pp452–455

  170. Chen D, Zhang L, Mathiopoulos PT, Huang X (2014) A methodology for automated segmentation and reconstruction of urban 3-D buildings from ALS point clouds. IEEE J Sel Top Appl Earth Obs Remote Sens 7(10):4199–4217

    Google Scholar 

  171. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    MathSciNet  Google Scholar 

  172. Li Y, Wu X, Chrysathou Y, Sharf A, Cohen-Or D, Mitra NJ (2011) Globfit: consistently fitting primitives by discovering global relations. In: ACM transactions on graphics (TOG), vol 30, ACM, p 52

  173. Schnabel R, Degener P, Klein R (2009) Completion and reconstruction with primitive shapes. In: Computer graphics forum, vol 28, Wiley Online Library, pp 503–512

  174. Schnabel R, Wahl R, Klein R (2007) Efficient RANSAC for point‐cloud shape detection. In: Computer graphics forum, vol 26, Wiley Online Library, pp 214–226

  175. Schnabel R, Wahl R, Klein R (2007) RANSAC based out-of-core point-cloud shape detection for city-modeling. In: Proceedings of terrestrisches laserscanning, vol 26, pp 214–226

  176. Tarsha-Kurdi F, Landes T, Grussenmeyer P (2007) Hough-transform and extended ransac algorithms for automatic detection of 3d building roof planes from lidar data. In: ISPRS workshop on laser scanning 2007 and SilviLaser 2007, vol 36, pp 407–412

  177. Tarsha-Kurdi F, Landes T, Grussenmeyer P (2008) Extended RANSAC algorithm for automatic detection of building roof planes from LiDAR data. Photogramm J Finl 21(1):97–109

    Google Scholar 

  178. Gelfand N, Guibas LJ (2004) Shape segmentation using local slippage analysis. In: Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on geometry processing, ACM, pp 214–223

  179. Benkő P, Várady T (2004) Segmentation methods for smooth point regions of conventional engineering objects. Comput Aided Des 36(6):511–523

    Google Scholar 

  180. Elberink SO, Vosselman G (2006) Adding the third dimension to a topographic database using airborne laser scanner data. Int Arch Photogramm Remote Sens Spat Inf Sci 36:92–97

    Google Scholar 

  181. Roggero M (2002) Object segmentation with region growing and principal component analysis. Int Arch Photogramm Remote Sens Spat Inf Sci 34(3):289–294

    Google Scholar 

  182. Vieira M, Shimada K (2005) Surface mesh segmentation and smooth surface extraction through region growing. Comput Aided Geom Des 22(8):771–792

    MathSciNet  MATH  Google Scholar 

  183. Lavoué G, Dupont F, Baskurt A (2005) A new CAD mesh segmentation method, based on curvature tensor analysis. Comput Aided Des 37(10):975–987

    MATH  Google Scholar 

  184. Sapkota PP (2008) Segmentation of coloured point cloud data. ITC

  185. Jagannathan A, Miller EL (2007) Three-dimensional surface mesh segmentation using curvedness-based region growing approach. IEEE Trans Pattern Anal Mach Intell 29(12):2195–2204

    Google Scholar 

  186. Czerniawski T, Nahangi M, Haas C, Walbridge S (2016) Pipe spool recognition in cluttered point clouds using a curvature-based shape descriptor. Autom Constr 71:346–358

    Google Scholar 

  187. Sharif MM, Nahangi M, Haas C, West J (2017) Automated model-based finding of 3D objects in cluttered construction point cloud models. Comput Aided Civ Infrastruct Eng 32(11):893–908

    Google Scholar 

  188. Wang C, Cho YK, Kim C (2015) Automatic BIM component extraction from point clouds of existing buildings for sustainability applications. Autom Constr 56:1–13

    Google Scholar 

  189. Sanchez V, Zakhor A (2012) Planar 3D modeling of building interiors from point cloud data. In: Image processing (ICIP), 2012 19th IEEE international conference on, IEEE, pp 1777–1780

  190. Pu S, Vosselman G (2009) Knowledge based reconstruction of building models from terrestrial laser scanning data. ISPRS J Photogramm Remote Sens 64(6):575–584

    Google Scholar 

  191. Quintana B, Prieto S, Adán A, Bosché F (2018) Door detection in 3D coloured point clouds of indoor environments. Autom Constr 85:146–166

    Google Scholar 

  192. Xu Y, Tuttas S, Hoegner L, Stilla U (2018) Reconstruction of scaffolds from a photogrammetric point cloud of construction sites using a novel 3D local feature descriptor. Autom Constr 85:76–95

    Google Scholar 

  193. Xiong X, Adan A, Akinci B, Huber D (2013) Automatic creation of semantically rich 3D building models from laser scanner data. Autom Constr 31:325–337

    Google Scholar 

  194. Chen J, Fang Y, Cho YK, Kim C (2016) Principal axes descriptor for automated construction-equipment classification from point clouds. J Comput Civ Eng 31(2):04016058

    Google Scholar 

  195. Bosche F, Haas CT (2008) Automated retrieval of 3D CAD model objects in construction range images. Autom Constr 17(4):499–512

    Google Scholar 

  196. Bosché F (2010) Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction. Adv Eng Inform 24(1):107–118

    Google Scholar 

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Authors and Affiliations

  1. Department of Building, School of Design and Environment, National University of Singapore, Singapore, Singapore

    Qian Wang

  2. College of Civil Engineering, Shenzhen University, Shenzhen, China

    Yi Tan

  3. Guangdong Mao-Bridge Design and Research Institute Co., Ltd., Shenzhen, Guangdong, China

    Yi Tan

  4. School of Economics and Management, Chongqing Jiaotong University, Chongqing, China

    Zhongya Mei

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  1. Qian Wang
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Wang, Q., Tan, Y. & Mei, Z. Computational Methods of Acquisition and Processing of 3D Point Cloud Data for Construction Applications. Arch Computat Methods Eng 27, 479–499 (2020). https://doi.org/10.1007/s11831-019-09320-4

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