Three-dimensional image-based approach for imperfect structures surface modeling

  • Irving D. Hernández
  • Murilo A. VazEmail author
  • Julio C. R. Cyrino
  • Nain M. R. Alvarez
Technical Paper


Great attention has been given, in the scientific literature, to the effect of initial imperfections on the structural behavior of experimental samples under compression loads. Geometrical reconstruction of the as-built surface is therefore required to allow accurate numerical modeling. High-precision systems for inverse engineering are expensive, and most of the times geometrical imperfections are rather complex to be described without high computational efforts. In this paper, an image-based approach to model the surface of imperfect structures using open-source software and a common digital camera is presented. The proposed approach aims to generate high-quality surfaces from real imperfect structures, by employing the surface-from-motion and multi-view stereo techniques. A controlled frame capture is introduced to decrease both the computational effort and number of repeated correspondences. The surface fit is then computed by meshing the dense cloud of points and adjusting several surface regions describing the samples’ profiles. The procedure is illustrated by using two damaged tubular member samples reconstructed by the proposed approach. Then, resulted geometries are verified by comparing measures from a three-dimensional high-precision laser scanner and a common mechanical procedure. Finally, a comparison of three-dimensional mappings of a ship panel with proposed approach and commercial photogrammetry software is performed. Verification results refer to the possibility of prototyping geometry surfaces from real imperfect samples by using inexpensive hardware and public domain software tools with acceptable level of accuracy.


Imperfect structures Damaged structures 3D modeling Reverse engineering Photogrammetry 



The support provided by the National Council of Scientific and Technological Development (CNPq) is gratefully acknowledged.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Gordon SJ, Lichti DD (2007) Modeling terrestrial laser scanner data for precise structural deformation measurement. J Surv Eng 133:72–80. CrossRefGoogle Scholar
  2. 2.
    Son S, Park H, Lee KH (2002) Automated laser scanning system for reverse engineering and inspection. Int J Mach Tools Manuf 42:889–897. CrossRefGoogle Scholar
  3. 3.
    Herráez J, Martínez JC, Coll E et al (2016) 3D modeling by means of videogrammetry and laser scanners for reverse engineering. Measurement 87:216–227. CrossRefGoogle Scholar
  4. 4.
    Lee IS, Lee JO, Park HJ, Bae KH (2010) Investigations into the influence of object characteristics on the quality of terrestrial laser scanner data. KSCE J Civ Eng 14:905–913. CrossRefGoogle Scholar
  5. 5.
    Jung S, Song S, Chang M, Park S (2018) Range image registration based on 2D synthetic images. Comput Aided Des 94:16–27. CrossRefGoogle Scholar
  6. 6.
    Papadopoulos V, Papadrakakis M (2005) The effect of material and thickness variability on the buckling load of shells with random initial imperfections. Comput Methods Appl Mech Eng 194:1405–1426. CrossRefzbMATHGoogle Scholar
  7. 7.
    Valença J, Júlio ENBS, Araújo HJ (2012) Applications of photogrammetry to structural assessment. Exp Tech 36:71–81. CrossRefGoogle Scholar
  8. 8.
    Paulo RMF, Teixeira-Dias F, Valente RAF (2013) Numerical simulation of aluminium stiffened panels subjected to axial compression: sensitivity analyses to initial geometrical imperfections and material properties. Thin Walled Struct 62:65–74. CrossRefGoogle Scholar
  9. 9.
    Gates TS (1986) Photogrammetry as a means of mapping postbuckled composite surfaces. Exp Tech 10:18–22. CrossRefGoogle Scholar
  10. 10.
    Rigo P, Sarghiuta R, Estefen S et al (2003) Sensitivity analysis on ultimate strength of aluminium stiffened panels. Mar Struct 16:437–468. CrossRefGoogle Scholar
  11. 11.
    Lueke JS, Sun P, Carey BD, Ariaratnam ST (2013) Validation of photogrammetric monitoring for trenchless construction applications. J Pipeline Syst Eng Pract 4:24–31. CrossRefGoogle Scholar
  12. 12.
    Vaz MA, Cyrino JCRR, Hernández ID et al (2018) Experimental and numerical analyses of the ultimate compressive strength of perforated offshore tubular members. Mar Struct 58:1–17. CrossRefGoogle Scholar
  13. 13.
    Otto KN, Wood KL (1998) Product evolution: a reverse engineering and redesign methodology. Res Eng Des 10:226–243. CrossRefGoogle Scholar
  14. 14.
    Sokovic M, Kopac J (2006) RE (reverse engineering) as necessary phase by rapid product development. J Mater Process Technol 175:398–403. CrossRefGoogle Scholar
  15. 15.
    Matta AK, Raju DR, Suman KNS (2015) The integration of CAD/CAM and rapid prototyping in product development: a review. Mater Today Proc 2:3438–3445. CrossRefGoogle Scholar
  16. 16.
    Cheng ZQ, Thacker JG, Pilkey WD et al (2001) Experiences in reverse-engineering of a finite element automobile crash model. Finite Elem Anal Des 37:843–860. CrossRefzbMATHGoogle Scholar
  17. 17.
    Zlatanova S (2008) Working Group II: acquisition—position paper: data collection and 3D reconstruction. In: van Oosterom P, Zlatanova S, Penninga F, Fendel EM (eds) Advances in 3D geoinformation systems. Springer, Berlin, pp 425–428CrossRefGoogle Scholar
  18. 18.
    Zhang YF, Wong YS, Loh HT (2008) Relationship between reverse engineering and rapid prototyping. In: Reverse engineering, Springer, London, pp 119–139Google Scholar
  19. 19.
    Seitz SM, Curless B, Diebel J et al (2006) A comparison and evaluation of multi-view stereo reconstruction algorithms. In: IEEE computer society conference on computer vision and pattern recognition (CVPR’06), vol 1, IEEE, pp 519–528Google Scholar
  20. 20.
    Westoby MJ, Brasington J, Glasser NF et al (2012) Structure-from-motion’ photogrammetry: a low-cost, effective tool for geoscience applications. Geomorphology 179:300–314. CrossRefGoogle Scholar
  21. 21.
    Debevec PE, Taylor CJ, Malik J (1996) Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach. In: SIGGRAPH, pp 11–20Google Scholar
  22. 22.
    Habbecke M, Kobbelt L (2007) A surface-growing approach to multi-view stereo reconstruction. In: Computer vision and pattern recognition—CVPR, pp 1–8Google Scholar
  23. 23.
    Snavely N, Seitz SM, Szeliski R (2008) Modeling the world from internet photo collections. Int J Comput Vis 80:189–210. CrossRefGoogle Scholar
  24. 24.
    DNV (2011) Offshore standard DNV-OS-C101: design of offshore steel structures, general (LRFD method), Det Norske VeritasGoogle Scholar
  25. 25.
    API (2014) Recommended practice 2A-WSD: planning, designing and constructing fixed offshore platforms. Working stress design, vol 22Google Scholar
  26. 26.
    Billingham J, Sharp JV, Spurrier J, Kilgallon PJ (2003) Research report 105: review of the performance of high strength steels used offshore. Health and Safety Executive (HSE), CranfieldGoogle Scholar
  27. 27.
    Szeliski R (2011) Computer vision—algorithms and applications. Springer, LondonzbMATHGoogle Scholar
  28. 28.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110. CrossRefGoogle Scholar
  29. 29.
    Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the seventh IEEE international conference on computer vision, vol 2, IEEE, pp 1150–1157Google Scholar
  30. 30.
    Huang S, Zhang Z, Ke T et al (2015) Scanning photogrammetry for measuring large targets in close range. Remote Sens 7:10042–10077. CrossRefGoogle Scholar
  31. 31.
    Moulon P, Monasse P, Marlet R (2013) Adaptive structure from motion with a contrario model estimation, pp 257–270CrossRefGoogle Scholar
  32. 32.
    Regard3D (2018) 3D reconstruction framework v0.9.5Google Scholar
  33. 33.
    Luong Q-T, Faugeras OD (1996) The fundamental matrix: theory, algorithms, and stability analysis. Int J Comput Vis 17:43–75. CrossRefGoogle Scholar
  34. 34.
    Stroppa L, Cristalli C (2017) Stereo vision system for accurate 3D measurements of connector pins’ positions in production lines. Exp Tech 41:69–78. CrossRefGoogle Scholar
  35. 35.
    Goesele M, Snavely N, Curless B et al (2007) Multi-view stereo for community photo collections. In: 2007 IEEE 11th international conference on computer vision, IEEE, pp 1–8Google Scholar
  36. 36.
    Fuhrmann S, Langguth F, Goesele M (2014) MVE: a multi-view reconstruction environment. In: Proceedings of the eurographics workshop on graphics and cultural heritage, Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, pp 11–18Google Scholar
  37. 37.
    Fuhrmann S, Langguth F, Moehrle N et al (2015) MVE—an image-based reconstruction environment. Comput Gr 53:44–53. CrossRefGoogle Scholar
  38. 38.
    Cignoni P, Callieri M, Corsini M et al (2008) MeshLab: an open-source mesh processing tool. In: Scarano V, Chiara RD, Erra U (eds) Eurographics Italian chapter conference, The Eurographics AssociationGoogle Scholar
  39. 39.
    Software GPL (2016) CloudCompare (v2.7.0)Google Scholar
  40. 40.
    Yilmaz HM (2010) Close range photogrammetry in volume computing. Exp Tech 34:48–54. CrossRefGoogle Scholar
  41. 41.
    Montgomery DC (2017) Design and analysis of experiments. Wiley, New YorkGoogle Scholar
  42. 42.
    Gonçalves JA, Henriques R (2015) UAV photogrammetry for topographic monitoring of coastal areas. ISPRS J Photogramm Remote Sens 104:101–111. CrossRefGoogle Scholar
  43. 43.
    Kwon S, Park J-W, Moon D et al (2017) Smart merging method for hybrid point cloud data using UAV and LIDAR in earthwork construction. Procedia Eng 196:21–28. CrossRefGoogle Scholar
  44. 44.
    Quan Li X, An Chen Z, Ting Zhang L, Jia D (2016) Construction and accuracy test of a 3D model of non-metric camera images using Agisoft Photoscan. Procedia Environ Sci 36:184–190. CrossRefGoogle Scholar
  45. 45.
    Leon JX, Roelfsema CM, Saunders MI, Phinn SR (2015) Measuring coral reef terrain roughness using ‘Structure-from-Motion’ close-range photogrammetry. Geomorphology 242:21–28. CrossRefGoogle Scholar
  46. 46.
    Agisoft (2015) AgiSoft PhotoScan StandardGoogle Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2019

Authors and Affiliations

  1. 1.Ocean Structures Laboratory NEOFederal University of Rio de Janeiro, Centro de TecnologiaRio de JaneiroBrazil

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