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Workshop of the European Group for Intelligent Computing in Engineering

EG-ICE 2018: Advanced Computing Strategies for Engineering pp 37–60Cite as

3D Imaging in Construction and Infrastructure Management: Technological Assessment and Future Research Directions

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10863))

Abstract

With rapid developments in 3D imaging technology, as well as the evolving need in Architecture/Engineering/Construction/Facility Management (AEC/FM) industry to understand various field aspects from a 3D perspective, several technologies, such as laser scanning, camera and RGBD camera, are becoming important components of civil engineers’ and architects’ toolbox. With improvements in efficiency and reduction in operational cost, new avenues are opening in leveraging 3D imaging sensors for construction and infrastructure management. In the light of this, there is a need to assess the achievements to date, especially with respect to unique challenging use case scenarios and requirements that construction and infrastructure management domains provide, and consequently identify potential research challenges that still need to be tackled. This paper targets doing such an assessment around several challenges faced when using 3D imaging to support construction and infrastructure management. It specifically discusses to what extent these challenges have been addressed and several approaches that are used in addressing them. It also presents current 3D imaging development trends and discusses briefly some challenges that are emerging due to increased application of 3D imaging techniques to highlight future research needs in this domain.

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References

  1. Baik, A.: From point cloud to Jeddah Heritage BIM Nasif Historical House – case study. Digit. Appl. Archaeol. Cult. Herit. 4, 1–18 (2017)

    Google Scholar 

  2. Grussenmeyer, P., Al Khalil, O.: From metric image archives to point cloud reconstruction: case study of the great Mosque of Aleppo in Syria. ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XLII-2/W5, pp. 295–301 (2017)

    Article  Google Scholar 

  3. Jaklič, A., Erič, M., Mihajlović, I., Stopinšek, Ž., Solina, F.: Volumetric models from 3D point clouds: the case study of sarcophagi cargo from a 2nd/3rd century AD Roman shipwreck near Sutivan on island Brač. Croatia. J. Archaeol. Sci. 62, 143–152 (2015)

    Article  Google Scholar 

  4. Xu, Y., He, J., Tuttas, S., Stilla, U.: Reconstruction of scaffolding components from photogrammetric point clouds of a construction site. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. II-3/W5, pp. 401–408 (2015)

    Google Scholar 

  5. Barazzetti, L.: Parametric as-built model generation of complex shapes from point clouds. Adv. Eng. Inf. 30, 298–311 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Becker, S., Peter, M., Fritsch, D.: Grammar-supported 3D indoor reconstruction from point clouds for “As-Built” Bim. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. II-3/W4, pp. 17–24 (2015)

    Google Scholar 

  8. Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Reconstructing building interiors from images. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 80–87 (2009)

    Google Scholar 

  9. Díaz-Vilariño, L., Khoshelham, K., Martínez-Sánchez, J., Arias, P.: 3D modeling of building indoor spaces and closed doors from imagery and point clouds. Sensors 15, 3491–3512 (2015)

    Article  Google Scholar 

  10. Xiao, J., Furukawa, Y.: Reconstructing the world’s museums. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 668–681. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33718-5_48

    Chapter  Google Scholar 

  11. Golparvar-Fard, M., Peña-Mora, F., Arboleda, C.A., Lee, S.: Visualization of construction progress monitoring with 4D simulation model overlaid on time-lapsed photographs. J. Comput. Civ. Eng. 23, 391–404 (2009)

    Article  Google Scholar 

  12. Turkan, Y., Bosché, F., Haas, C.T., Haas, R.: Tracking secondary and temporary concrete construction objects using 3D imaging technologies. In: Computing in Civil Engineering, pp. 749–756. American Society of Civil Engineers, Reston (2013)

    Google Scholar 

  13. Golparvar-Fard, M., Peña-Mora, F., Savarese, S.: Automated progress monitoring using unordered daily construction photographs and IFC-based building information models. J. Comput. Civ. Eng. 29, 4014025 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Teizer, J., Caldas, C.H., Haas, C.T.: Real-time three-dimensional occupancy grid modeling for the detection and tracking of construction resources. J. Constr. Eng. Manag. 133, 880–888 (2007)

    Article  Google Scholar 

  16. Kim, H., Kim, K., Kim, H.: Data-driven scene parsing method for recognizing construction site objects in the whole image. Autom. Constr. 71, 271–282 (2016)

    Article  Google Scholar 

  17. Anil, E.B., Tang, P., Akinci, B., Huber, D.: Deviation analysis method for the assessment of the quality of the as-is Building Information Models generated from point cloud data. Autom. Constr. 35, 507–516 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Chaiyasarn, K., Kim, T.-K., Viola, F., Cipolla, R., Soga, K.: Distortion-free image mosaicing for tunnel inspection based on robust cylindrical surface estimation through structure from motion. J. Comput. Civ. Eng. 30, 4015045 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Vidas, S., Moghadam, P., Bosse, M.: 3D thermal mapping of building interiors using an RGB-D and thermal camera. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2311–2318 (2013)

    Google Scholar 

  22. Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the world from Internet photo collections. Int. J. Comput. Vis. 80, 189–210 (2008)

    Article  Google Scholar 

  23. Pătrăucean, V., Armeni, I., Nahangi, M., Yeung, J., Brilakis, I., Haas, C.: State of research in automatic as-built modelling. Adv. Eng. Inf. 29, 162–171 (2015)

    Article  Google Scholar 

  24. Seo, J., Han, S., Lee, S., Kim, H.: Computer vision techniques for construction safety and health monitoring. Adv. Eng. Inf. 29, 239–251 (2015)

    Article  Google Scholar 

  25. Yang, J., Park, M.W., Vela, P.A., Golparvar-Fard, M.: Construction performance monitoring via still images, time-lapse photos, and video streams: now, tomorrow, and the future. Adv. Eng. Inf. 29, 211–224 (2015)

    Article  Google Scholar 

  26. Fathi, H., Dai, F., Lourakis, M.: Automated as-built 3D reconstruction of civil infrastructure using computer vision: achievements, opportunities, and challenges. Adv. Eng. Inf. 29, 149–161 (2015)

    Article  Google Scholar 

  27. Brilakis, I., Dai, F., Radopoulou, S.-C.: Achievements and challenges in recognizing and reconstructing civil infrastructure. In: Dellaert, F., Frahm, J.-M., Pollefeys, M., Leal-Taixé, L., Rosenhahn, B. (eds.) Outdoor and Large-Scale Real-World Scene Analysis. LNCS, vol. 7474, pp. 151–176. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34091-8_7

    Chapter  Google Scholar 

  28. Lu, Q., Lee, S.: Image-based technologies for constructing as-is building information models for existing buildings. J. Chem. Inf. Model. 31 (2017)

    Article  Google Scholar 

  29. Cho, Y.K., Ham, Y., Golpavar-Fard, M.: 3D as-is building energy modeling and diagnostics: a review of the state-of-the-art. Adv. Eng. Inf. 29, 184–195 (2015)

    Article  Google Scholar 

  30. Guo, H., Yu, Y., Skitmore, M.: Visualization technology-based construction safety management: a review. Autom. Constr. 73, 135–144 (2017)

    Article  Google Scholar 

  31. Mukupa, W., Roberts, G.W., Hancock, C.M., Al-Manasir, K.: A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures. Surv. Rev. 1–18 (2016)

    Google Scholar 

  32. Ham, Y., Han, K.K., Lin, J.J., Golparvar-Fard, M.: Visual monitoring of civil infrastructure systems via camera-equipped Unmanned Aerial Vehicles (UAVs): a review of related works. Vis. Eng. 4, 1 (2016)

    Article  Google Scholar 

  33. Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., Fieguth, P.: A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Adv. Eng. Inf. 29, 196–210 (2015)

    Article  Google Scholar 

  34. Rabbani, T., van den Heuvel, F. a, Vosselman, G.: Segmentation of point clouds using smoothness constraint. In: Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - Comm. V Symp. ’Image Eng. Vis. Metrol. 36, 248–253 (2006)

    Google Scholar 

  35. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)

    Article  Google Scholar 

  36. Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Reconstructing building interiors from images. In: 2009 IEEE 12th International Conference Computer Vision, pp. 80–87 (2009)

    Google Scholar 

  37. Armeni, I., Sener, O., Zamir, A., Jiang, H.: 3D semantic parsing of large-scale indoor spaces. CVPR, pp. 1534–1543 (2016)

    Google Scholar 

  38. Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ADE20K dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5122–5130 (2017)

    Google Scholar 

  39. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Li, F.-F.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)

    Google Scholar 

  40. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  41. Lazebnik, S., Schmid, C., Ponce, J., Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories To cite this version: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. (2010)

    Google Scholar 

  42. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neu- ral Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  43. Lehtola, V.V., Kaartinen, H., Nüchter, A., Kaijaluoto, R., Kukko, A., Litkey, P., Honkavaara, E., Rosnell, T., Vaaja, M.T., Virtanen, J.P., Kurkela, M., El Issaoui, A., Zhu, L., Jaakkola, A., Hyyppä, J.: Comparison of the selected state-of-the-art 3D indoor scanning and point cloud generation methods. Remote Sens. 9, 796 (2017)

    Article  Google Scholar 

  44. Yang, S.W., Wang, C.C.: Dealing with laser scanner failure: mirrors and windows. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3009–3015 (2008)

    Google Scholar 

  45. Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., Leonard, J.J.: Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans. Robot. 32, 1309–1332 (2016)

    Article  Google Scholar 

  46. Książek, M.V., Nowak, P.O., Kivrak, S., Rosłon, J.H., Ustinovichius, L.: Computer-aided decision-making in construction project development. J. Civ. Eng. Manag. 21, 248–259 (2015)

    Article  Google Scholar 

  47. Larsson, S., Kjellander, J.A.P.: Path planning for laser scanning with an industrial robot. Rob. Auton. Syst. 56, 615–624 (2008)

    Article  Google Scholar 

  48. Landa, Y., Tsai, R.: Visibility of point clouds and exploratory path planning in unknown environments. Commun. Math. Sci. 6, 881–913 (2008)

    Article  MathSciNet  Google Scholar 

  49. Arora, S., Scherer, S.: Randomized algorithm for informative path planning with budget constraints. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 4997–5004 (2017)

    Google Scholar 

  50. Akinci, B., Boukamp, F., Gordon, C., Huber, D., Lyons, C., Park, K.: A formalism for utilization of sensor systems and integrated project models for active construction quality control. Autom. Constr. 15, 124–138 (2006)

    Article  Google Scholar 

  51. Liu, T., Carlberg, M., Chen, G., Chen, J., Kua, J., Zakhor, A.: Indoor localization and visualization using a human-operated backpack system. In: 2010 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2010 - Conference Proceedings, pp. 1–10. IEEE (2010)

    Google Scholar 

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

    Article  Google Scholar 

  53. Drost, B., Ulrich, M., Navab, N., Ilic, S.: Model globally, match locally: efficient and robust 3D object recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 998–1005. IEEE (2010)

    Google Scholar 

  54. Diaz-Vilarino, L., Boguslawski, P., Khoshelham, K., Lorenzo, H., Mahdjoubi, L.: Indoor navigation from point clouds: 3D modelling and Obstacle Detection. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch. 41, pp. 275–281 (2016)

    Article  Google Scholar 

  55. Kasireddy, V., Akinci, B.: Challenges in generation of as-is bridge information model: a case study. In: Proceedings of the 32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future (2015)

    Google Scholar 

  56. Velodyne LiDAR HDL-64E. http://velodynelidar.com/hdl-64e.html

  57. Yoder, L., Scherer, S.: Autonomous exploration for infrastructure modeling with a micro aerial vehicle. In: Wettergreen, D.S., Barfoot, T.D. (eds.) Field and Service Robotics. STAR, vol. 113, pp. 427–440. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-27702-8_28

    Chapter  Google Scholar 

  58. Bosché, F., Ahmed, M., Turkan, Y., Haas, C.T., Haas, R.: 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 (2015)

    Article  Google Scholar 

  59. Liu, T., Carlberg, M., Chen, G., Chen, J., Kua, J., Zakhor, A.: Indoor localization and visualization using a human-operated backpack system. In: Proceedings of the 2010 International Conference on Indoor Positioning and Indoor Navigation. IPIN 2010, pp. 15–17 (2010)

    Google Scholar 

  60. Metni, N., Hamel, T.: A UAV for bridge inspection: visual servoing control law with orientation limits. Autom. Constr. 17, 3–10 (2007)

    Article  Google Scholar 

  61. Maturana, D., Scherer, S.: VoxNet: A 3D convolutional neural network for real-time object recognition. Iros, pp. 922–928 (2015)

    Google Scholar 

  62. Armeni, I., Sener, O., Zamir, A., Jiang, H.: 3D semantic parsing of large-scale indoor spaces. In: CVPR, pp. 1534–1543 (2016)

    Google Scholar 

  63. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation (2016)

    Google Scholar 

  64. Hong, S., Jung, J., Kim, S., Cho, H., Lee, J., Heo, J.: Semi-automated approach to indoor mapping for 3D as-built building information modeling. Comput. Environ. Urban Syst. 51, 34–46 (2015)

    Article  Google Scholar 

  65. Han, K.K., Golparvar-Fard, M.: Appearance-based material classification for monitoring of operation-level construction progress using 4D BIM and site photologs. Autom. Constr. 53, 44–57 (2015)

    Article  Google Scholar 

  66. Irschara, A., Zach, C., Frahm, J.-M., Bischof, H.: From structure-from-motion point clouds to fast location recognition. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2599–2606 (2009)

    Google Scholar 

  67. Sattler, T., Leibe, B., Kobbelt, L.: Fast image-based localization using direct 2D-to-3D matching. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 667–674 (2011)

    Google Scholar 

  68. Dai, F., Rashidi, A., Brilakis, J., Vela, P.: Comparison of image-based and time-of-flight-based technologies for 3D reconstruction of infrastructure (2012). http://ascelibrary.org/doi/10.1061/%28ASCE%29CO.1943-7862.0000565

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

    Article  Google Scholar 

  70. Khoshelham, K., Elberink, S.O.: Accuracy and resolution of kinect depth data for indoor mapping applications. Sensors 12, 1437–1454 (2012)

    Article  Google Scholar 

  71. Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1–4. IEEE (2011)

    Google Scholar 

  72. Martinez, A., Fernández, E.: Learning ROS for Robotics Programming. Packt Publishing Ltd., Birmingham (2013)

    Google Scholar 

  73. Radu, R.: The PCD (Point Cloud Data) file format. http://pointclouds.org/documentation/tutorials/pcd_file_format.php

  74. Lee, S.H., Kim, B.G.: IFC extension for road structures and digital modeling. Procedia Eng. 14, 1037–1042 (2011)

    Article  Google Scholar 

  75. Hackel, T., Savinov, N., Ladicky, L., Wegner, J.D., Schindler, K., Pollefeys, M.: SEMANTIC3D.net: a new large-scale point cloud classification benchmark

    Google Scholar 

  76. Rusu, R.B., Blodow, N., Beetz, M.: Fast Point Feature Histograms (FPFH) for 3D registration. In: 2009 IEEE International Conference on Robotics and Automation, pp. 3212–3217 (2009)

    Google Scholar 

  77. Rusu, R.B., Bradski, G., Thibaux, R., Hsu, J.: Fast 3D recognition and pose using the viewpoint feature histogram. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2155–2162. IEEE (2010)

    Google Scholar 

  78. Mastin, A., Kepner, J., Fisher Iii, J.: Automatic registration of LIDAR and optical images of urban scenes. In: CVPR (2009)

    Google Scholar 

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

    Article  Google Scholar 

  80. Park, Y., Yun, S., Won, C.S., Cho, K., Um, K., Sim, S.: Calibration between color camera and 3D LIDAR instruments with a polygonal planar board. Sensors 14, 5333–5353 (2014)

    Article  Google Scholar 

  81. Stamos, I., Allen, P.K.: Geometry and texture recovery of scenes of large scale. Comput. Vis. Image Underst. 88, 94–118 (2002)

    Article  Google Scholar 

  82. J. Huang, S.Y.: Point cloud labeling using 3D convolutional neural network. In: International Conference on Pattern Recognition, pp. 1–6 (2016)

    Google Scholar 

  83. Pan, S.J., Yang, Q.: A survey on transfer learning (2010). https://www.cse.ust.hk/~qyang/Docs/2009/tkde_transfer_learning.pdf

  84. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: CVPR (2016)

    Google Scholar 

  85. Yan, Y., Guldur, B., Yoder, L., Kasireddy, V., Huber, D., Scherer, S., Akinci, B., Hajjar, J.F.: Automated damage detection and structural modelling with laser scanning. In: Structural Stability Research Council Annual Stability Conference 2016, SSRC 2016 (2016)

    Google Scholar 

  86. Kasireddy, V., Akinci, B.: A case study on comparative analysis of 3D point clouds from UAV mounted and terrestrial scanners for bridge condition assessment. In: Proceedings of the Lean & Computing in Construction Congress (LC3). CIB W78, Heraklion, Greece (2017) (accepted)

    Google Scholar 

  87. Dai, F., Rashidi, A., Brilakis, J., Vela, P.: Comparison of image-based and time-of-flight-based technologies for 3D reconstruction of infrastructure. Constr. Res. Congr. 139, 929–939 (2012)

    Google Scholar 

  88. Huang, J., Wang, Z., Gao, J., Huang, Y., Towers, D.P.: High-precision registration of point clouds based on sphere feature constraints. Sensors 17, 72 (2017)

    Article  Google Scholar 

  89. Seitz, S.M., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, vol. 1, pp. 519–528 (2006)

    Google Scholar 

  90. Succar, B.: Building information modelling framework: a research and delivery foundation for industry stakeholders. Autom. Constr. 18, 357–375 (2009)

    Article  Google Scholar 

  91. Huber, D.: The ASTM E57 file format for 3D imaging data exchange. In: Three-Dimensional Imaging, Interaction, and Measurement (2011)

    Google Scholar 

  92. Kiziltas, S., Akinci, B., Ergen, E., Tang, P., Gordon, C.: Technological assessment and process implications of field data capture technologies for construction and facility/infrastructure management. Electron. J. Inf. Technol. Constr. 13, 134–154 (2008)

    Google Scholar 

  93. Wedding, J., Probert, D.: Mastering AutoCAD Civil 3D 2009. Wiley, Chichester (2008)

    Google Scholar 

  94. Khemlani, L.: Autodesk Revit: implementation in practice. White Pap. Autodesk (2004)

    Google Scholar 

  95. Tang, P., Anil, E.B., Akinci, B., Huber, D.: Efficient and effective quality assessment of as-is building information models and 3D laser-scanned data. In: Computing in Civil Engineering, pp. 486–493 (2011)

    Google Scholar 

  96. Anil, E.B., Tang, P., Akinci, B., Huber, D.: Assessment of quality of as-is building information models generated from point clouds using deviation analysis. In: Environmental Engineering, vol. 7864, p. 78640F–13 (2011)

    Google Scholar 

  97. Autodesk Inc.: Autodesk Recap (2015)

    Google Scholar 

  98. Velodyne LiDAR HDL-64E. http://hypertech.co.il/wp-content/uploads/2015/12/HDL-64E-Data-Sheet.pdf

  99. Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Robot. 31, 1147–1163 (2015)

    Article  Google Scholar 

  100. Bae, H., Golparvar-Fard, M., White, J.: Image-based localization and content authoring in structure-from-motion point cloud models for real-time field reporting applications. J. Comput. Civ. Eng. 29, B4014008 (2015)

    Article  Google Scholar 

  101. Olsen, M.J., Kuester, F., Chang, B.J., Hutchinson, T.C.: Terrestrial laser scanning-based structural damage assessment. J. Comput. Civ. Eng. 24, 264–272 (2010)

    Article  Google Scholar 

  102. Teza, G., Galgaro, A., Moro, F.: Contactless recognition of concrete surface damage from laser scanning and curvature computation. NDT E Int. 42(4), 240–249 (2009)

    Article  Google Scholar 

  103. Liu, W., Chen, S., Hauser, E.: LiDAR-based bridge structure defect detection. Exp. Tech. 35, 27–34 (2011)

    Article  Google Scholar 

  104. Tang, P., Akinci, B.: Automatic execution of workflows on laser-scanned data for extracting bridge surveying goals. Adv. Eng. Inf. 26, 889–903 (2012)

    Article  Google Scholar 

  105. Tang, P., Chen, G., Shen, Z., Ganapathy, R.: A spatial-context-based approach for automated spatial change analysis of piece-wise linear building elements. Comput. Civ. Infrastruct. Eng. 31, 65–80 (2016)

    Article  Google Scholar 

  106. Chen, S.: Laser Scanning Technology for Bridge Monitoring. InTech (2012)

    Google Scholar 

  107. Laefer, D.F., Truong-Hong, L., Carr, H., Singh, M.: Crack detection limits in unit based masonry with terrestrial laser scanning. NDT E Int. 62, 66–76 (2014)

    Article  Google Scholar 

  108. Loprencipe, G., Cantisani, G.: Evaluation methods for improving surface geometry of concrete floors: a case study. Case Stud. Struct. Eng. 4, 14–25 (2015)

    Article  Google Scholar 

  109. Kayen, R., Pack, R.T., Bay, J., Sugimoto, S., Tanaka, H.: Terrestrial-LIDAR visualization of surface and structural deformations of the 2004 Niigata Ken Chuetsu, Japan, earthquake. Earthq. Spectra. 22, 147–162 (2006)

    Article  Google Scholar 

  110. Olsen, M.J., Kayen, R.: Post-Earthquake and Tsunami 3D laser scanning forensic investigations. Forensic Eng. 2012, 477–486 (2012)

    Article  Google Scholar 

  111. Kashani, A., Crawford, P.: Automated tornado damage assessment and wind speed estimation based on terrestrial laser scanning. J. Comput. Civ. Eng. 29, 1–10 (2014)

    Google Scholar 

  112. Anil, E.B., Akinci, B., Huber, D.: Representation requirements of as-is building information models generated from laser scanned point cloud data. In: Proceedings of the International Symposium on Automation and Robotics in Construction (ISARC), Seoul, Korea (2011)

    Google Scholar 

  113. Kasireddy, V., Akinci, B.: Towards the integration of inspection data with bridge information models to support visual condition assessment. In: Proceedings of the Congress on Computing in Civil Engineering, pp. 644–651. American Society of Civil Engineers, Reston (2015)

    Google Scholar 

  114. Sermanet, P., LeCun, Y.: Traffic sign recognition with multi-scale convolutional networks. In: Neural Networks (IJCNN) (2011)

    Google Scholar 

  115. Protopapadakis, E., Doulamis, N.: Image based approaches for tunnels’ defects recognition via robotic inspectors. In: Bebis, G., et al. (eds.) ISVC 2015. LNCS, vol. 9474, pp. 706–716. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27857-5_63

    Chapter  Google Scholar 

  116. Maeda, H., Sekimoto, Y., Seto, T.: An easy infrastructure management method using on-board smartphone images and citizen reports by deep neural network. In: IoT in Urban Space, pp. 111–113. ACM Press, New York (2016)

    Google Scholar 

  117. Bang, S., Kim, H., Kim, H.: UAV-based automatic generation of high-resolution panorama at a construction site with a focus on preprocessing for image stitching. Autom. Constr. 84, 70–80 (2017)

    Article  Google Scholar 

  118. Malihi, S., Valadan Zoej, M.J., Hahn, M., Mokhtarzade, M., Arefi, H.: 3D building reconstruction using dense photogrammetric point cloud. ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XLI-B3, pp. 71–74 (2016)

    Google Scholar 

  119. Zhao, K., Iurgel, U., Meuter, M., Pauli, J.: An automatic online camera calibration system for vehicular applications. In: 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014, pp. 1490–1492. IEEE (2014)

    Google Scholar 

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Acknowledgements

The project is funded by a grant from the National Science Foundation (NSF), # 1534114. NSF’s support is gratefully acknowledged. Any opinions, findings, conclusions or recommendations presented in this paper are those of authors and do not necessarily reflect the views of the NSF.

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Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Yujie Wei & Varun Kasireddy

  2. Paul P. Christiano Professor, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Burcu Akinci

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  1. Yujie Wei
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  2. Varun Kasireddy
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  3. Burcu Akinci
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Correspondence to Burcu Akinci .

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  1. Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology, Lausanne (EPFL), Lausanne, Switzerland

    Ian F. C. Smith

  2. Institute for Landscape, Architecture, Construction and Territory (inPact) Construction and Environment Department (CED), University of Applied Sciences, Geneva (HEPIA), Geneva, Switzerland

    Bernd Domer

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Wei, Y., Kasireddy, V., Akinci, B. (2018). 3D Imaging in Construction and Infrastructure Management: Technological Assessment and Future Research Directions. In: Smith, I., Domer, B. (eds) Advanced Computing Strategies for Engineering. EG-ICE 2018. Lecture Notes in Computer Science(), vol 10863. Springer, Cham. https://doi.org/10.1007/978-3-319-91635-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-91635-4_3

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