Deformation Tracking in 3D Point Clouds Via Statistical Sampling of Direct Cloud-to-Cloud Distances


Dense three-dimensional (3D) point clouds of infrastructure systems, generated from laser scanners or through multi-view photogrammetry, have significant potential as a source of nondestructive evaluation information. The growing maturity of these techniques make them capable of reconstructing photorealistic 3D models with accuracy on the millimeter scale, adequate for inspection and evaluation practices. Manual analysis of these point clouds is often time consuming and labor intensive and does not provide explicit information on structural performance and health conditions, highlighting the need for new techniques to efficiently analyze these models. This paper presents a new 3D point cloud change analysis approach for tracking small movements over time through localized spatial analytics. This technique uses a combination of a direct point-wise distance metric in conjunction with statistical sampling to extract structural deformations. By identifying and tracking these changes, mechanical deformations can be quantified along with the associated strains and stresses. These measurements can then be used to assess both service conditions and remaining system capacity. The results of a series of laboratory experiments designed to test the proposed approach are presented as well. The findings indicate measurement accuracy on the order of +/− 0.2 mm (95% confidence interval), making it suitable for accurate and automatic geometrical analyses and change detection in a variety of infrastructure inspection scenarios. Ongoing work seeks to connect this technique to automated finite element model updating, and to field test the measurement technique.

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  1. 1.

    Fathi, H., Dai, F., Lourakis, M.: Automated as-built 3D reconstruction of civil infrastructure using computer vision: achievements, opportunities, and challenges. Adv. Eng. Inform. 29(2), 149–167 (2015)

    Article  Google Scholar 

  2. 2.

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

  3. 3.

    Zhou, Z., Gong, J., Guo, M.: Image-based 3D reconstruction for posthurricane residential building damage assessment. J. Comput. Civ. Eng. 30(2), 04015015 (2016)

    Article  Google Scholar 

  4. 4.

    Ghahremani, K., Khaloo, A., Lattanzi, D.: Automated 3D image-based section loss detection for finite element model updating. In: 33rd International Symposium on Automation and Robotics in Construction, Auburn, AL, pp. 411–419 (2016)

  5. 5.

    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, Apr. (2016)

  6. 6.

    Qin, R., Tian, J., Reinartz, P.: 3D change detection—approaches and applications. ISPRS J. Photogramm. Remote Sens. 122, 41–56 (2016)

    Article  Google Scholar 

  7. 7.

    Lindenbergh, R., Pietrzyk, P.: Change detection and deformation analysis using static and mobile laser scanning. Appl. Geomat. 7(2), 65–74 (2015)

    Article  Google Scholar 

  8. 8.

    Fuchs, P., Washer, G., Chase, S., Moore, M.: Applications of laser-based instrumentation for highway bridges. J. Bridge Eng. 9(6), 541–549 (2004)

    Article  Google Scholar 

  9. 9.

    Cabaleiro, M., Riveiro, B., Arias, P., Caamaño, J.C.: Algorithm for beam deformation modeling from LiDAR data. Measurement 76, 20–31 (2015)

    Article  Google Scholar 

  10. 10.

    Liu, W., Chen, S.: Reliability analysis of bridge evaluations based on 3D light detection and ranging data. Struct. Control Health Monit. 20(12), 1397–1409 (2013)

    Article  Google Scholar 

  11. 11.

    Truong-Hong, L., Laefer, D.F.: Using terrestrial laser scanning for dynamic bridge deflection measurement. In: IABSE Istanbul Bridge Conference. Istanbul, Turkey 11–13, 2014 (2014)

  12. 12.

    Park, H.S., Lee, H.M., Adeli, H., Lee, I.: A new approach for health monitoring of structures: terrestrial laser scanning. Comput.-Aided Civ. Infrastruct. Eng. 22(1), 19–30 (2007)

  13. 13.

    Lindenbergh, R., Pfeifer, N.: A statistical deformation analysis of two epochs of terrestrial laser data of a lock. In: Proceedings of the 7th Conference on Optical, pp. 61–70 (2005)

  14. 14.

    Pesci, A., Teza, G., Bonali, E., Casula, G., Boschi, E.: A laser scanning-based method for fast estimation of seismic-induced building deformations. ISPRS J. Photogramm. Remote Sens. 79, 185–198 (2013)

    Article  Google Scholar 

  15. 15.

    Khaloo, A., Lattanzi, D.: Extracting structural models through computer vision. Struct. Congress 2015, 538–548 (2015)

    Google Scholar 

  16. 16.

    Cabaleiro, M., Riveiro, B., Arias, P., Caamaño, J.C.: Algorithm for the analysis of the geometric properties of cross-sections of timber beams with lack of material from LiDAR data. Mater. Struct. 49(10), 4265–4278 (2015)

    Article  Google Scholar 

  17. 17.

    Rusu, R.B., Marton, Z.C., Blodow, N., Dolha, M., Beetz, M.: Towards 3D Point cloud based object maps for household environments. Robot. Auton. Syst. 56(11), 927–941 (2008)

    Article  Google Scholar 

  18. 18.

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

  19. 19.

    Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the world from internet photo collections. Int. J. Comput. Vis. 80(2), 189–210 (2008)

    Article  Google Scholar 

  20. 20.

    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  21. 21.

    Hirschmüller, H., Buder, M., Ernst, I.: Memory efficient semi-global matching. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 3, 371–376 (2012)

    Article  Google Scholar 

  22. 22.

    Fuhrmann, S., Goesele, M.: Fusion of depth maps with multiple scales. In: Proceedings of the 2011 SIGGRAPH Asia Conference, New York, NY, USA, pp. 148:1–148:8 (2011)

  23. 23.

    Musialski, P., Wonka, P., Aliaga, D.G., Wimmer, M., van Gool, L., Purgathofer, W.: A survey of urban reconstruction. Comput. Graph. Forum 32(6), 146–177 (2013)

    Article  Google Scholar 

  24. 24.

    Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. Presented at the Robotics-DL tentative, vol. 1611, pp. 586–606 (1992)

  25. 25.

    Friedman, J.H., Bentley, J.L., Finkel, R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw. 3(3), 209–226 (1977)

  26. 26.

    Girardeau-Montaut, D., Roux, M., Marc, R., Thibault, G.: Change detection on points cloud data acquired with a ground laser scanner. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 36(3), W19 (2005)

  27. 27.

    Lague, D., Brodu, N., Leroux, J.: Accurate 3D comparison of complex topography with terrestrial laser scanner: application to the Rangitikei canyon (N-Z). ISPRS J. Photogramm. Remote Sens. 82, 10–26 (2013)

    Article  Google Scholar 

  28. 28.

    Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 850–863 (1993)

    Article  Google Scholar 

  29. 29.

    Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W.: Surface reconstruction from unorganized points. In: Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques, New York, NY, USA, pp. 71–78 (1992)

  30. 30.

    Golub, G.H., Reinsch, C.: Singular value decomposition and least squares solutions. Numer. Math. 14(5), 403–420 (1970)

    MathSciNet  Article  MATH  Google Scholar 

  31. 31.

    Agisoft PhotoScan. Professional Edition. Agisoft LLC (2016)

  32. 32.

    Richter, R., Kyprianidis, J.E., Döllner, J.: Out-of-core GPU-based change detection in massive 3D point clouds. Trans. GIS 17(5), 724–741 (2013)

    Google Scholar 

  33. 33.

    Tam, G.K.L., et al.: Registration of 3D point clouds and meshes: a survey from rigid to nonrigid. IEEE Trans. Vis. Comput. Graph. 19(7), 1199–1217 (2013)

    Article  Google Scholar 

  34. 34.

    Cignoni, P., Rocchini, C., Scopigno, R.: Metro: measuring error on simplified surfaces. Comput. Graph. Forum 17(2), 167–174 (1998)

    Article  Google Scholar 

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This material is based upon work partly supported by the National Science Foundation (NSF) [Grant No. CMMI-1433765] and the U.S. Forest Service [15-CS-11100100-015]. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the NSF or U.S. Forest Service.

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Correspondence to David Lattanzi.

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Jafari, B., Khaloo, A. & Lattanzi, D. Deformation Tracking in 3D Point Clouds Via Statistical Sampling of Direct Cloud-to-Cloud Distances. J Nondestruct Eval 36, 65 (2017).

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  • 3D change detection
  • Deformation analysis
  • Point clouds
  • Condition assessment
  • Remote sensing
  • 3D data processing
  • Structural monitoring