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Preparation and enhancement of 3D laser scanner data for realistic coloured BIM models

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Abstract

Realistic point cloud models are frequently required in order to create efficient 3D data processing algorithms. In a building information modelling context, for example, the segmentation and object recognition of point clouds become extremely difficult tasks when the inputs of the algorithms are not sufficiently good. This paper proposes a method with which to efficiently solve two of the most important issues during the creation of realistic 3D data with laser scanners: the treatment of specularity in coloured point clouds and the view merging process. We particularly deal with wide-range laser scanners with the objective of generating realistic orthoimages of the main structural elements of the insides of buildings (i.e. walls, ceilings and floors). The new algorithm gathers the coloured points belonging to structural elements and delimits the specular regions originating from non-controlled or directional lighting sources (e.g. flashes). The restoration of these specular regions is solved in a subsequent stage in which several partial views of a structural element are integrated into a unique orthoimage. Finally, the quality of the resulting orthoimage is evaluated by comparing it with the corresponding ground truth image. This method has been tested on a public database, yielding encouraging results.

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References

  1. Salvi, J., Matabosch, C., Fofi, D., Forest, J.: A review of recent range image registration methods with accuracy evaluation. Image Vis. Comput. 25(5), 578–596 (2007)

    Article  Google Scholar 

  2. Santamaría, J., Cordón, O., Damas, S.: A comparative study of state-of-the-art evolutionary image registration methods for 3D modeling. Comput. Vis. Image Underst. 115(9), 1340–1354 (2011)

    Article  Google Scholar 

  3. Besl, P., McKay, N.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Article  Google Scholar 

  4. Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. Int. J. Comput. Vis. 13(2), 119–152 (1994)

    Article  Google Scholar 

  5. Xiao, G., Ong, S.H., Foong, K.W.C.: Efficient partial-surface registration for 3D objects. Comput. Vis. Image Underst. 98(2), 271–294 (2005)

    Article  Google Scholar 

  6. Turk, G., Levoy, M.: Zippered polygon meshes from range images. In: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH’94), pp. 311–318. (1994)

  7. Di Angelo, L., Di Stefano, P., Giaccari, L.: A new mesh-growing algorithm for fast surface reconstruction. CAD Comput. Aided Des. 43(6), 639–650 (2011)

    Article  Google Scholar 

  8. Curless, B., Levoy, M.: A volumetric method for building complex models from range images. In: Proceedings of 23rd Annual Conference, pp. 303–312. (1996)

  9. Wolberg, G., Zokai, S.: PhotoSketch: a photocentric urban 3D modeling system. Vis. Comput. 34, 1–12 (2017)

    Google Scholar 

  10. Adan, A., Molina, F., Vazquez, A.S., Morena, L.: 3D feature tracking using a dynamic structured light system. In: The 2nd Canadian Conference on Computer and Robot Vision (CRV’05), pp. 168–175. (2005)

  11. Wang, L., Kang, S.B., Szeliski, R., Shum, H.-Y.: Optimal texture map reconstruction from multiple views. In: Proceedings of 2001 IEEE Computer Society Conference on Computer Visual Pattern Recognition, 2001. CVPR 2001, vol. 1, p. I-347-I-354. (2001)

  12. Callieri, M., Cignoni, P., Corsini, M., Scopigno, R.: Masked photo blending: mapping dense photographic data set on high-resolution sampled 3D models. Comput. Graph. 32(4), 464–473 (2008)

    Article  Google Scholar 

  13. Goldluecke, B., Cremers, D.: Superresolution texture maps for multiview reconstruction. In: Proceedings on IEEE International Conference on Computer Visual, no. Iccv, pp. 1677–1684. (2009)

  14. Liu, X.M., Peng, X., Yin, Y.K., Li, A.M., Liu, X.L., Wu, W.: Generation of Photorealistic 3D Image Using Optical Digitizer. Appl. Opt. 51, 1304–1311 (2012)

    Article  Google Scholar 

  15. Merchán, P., Adán, A., Salamanca, S., Domínguez, V., Chacón, R.: Geometric and colour data fusion for outdoor 3D models. Sensors (Switzerland) 12(6), 6893–6919 (2012)

    Article  Google Scholar 

  16. Wyngaerd, J.V., Van Gool, L.: Combining texture and shape for automatic crude patch registration. In: Proceedings on International Conference on 3-D Digital Imaging Model. 3DIM, vol. 2003, pp. 179–186. (2003)

  17. Bannai, N., Fisher, R.B., Agathos, A.: Multiple color texture map fusion for 3D models. Pattern Recognit. Lett. 28(6), 748–758 (2007)

    Article  Google Scholar 

  18. Troccoli, A., Allen, P.: Building illumination coherent 3D models of large-scale outdoor scenes. Int. J. Comput. Vis. 78(2–3), 261–280 (2008)

    Article  Google Scholar 

  19. Dellepiane, M., Callieri, M., Corsini, M., Cignoni, P., Scopigno, R.: Improved color acquisition and mapping on 3D models via flash-based photography. J. Comput. Cult. Herit. 2(4), 1–20 (2010)

    Article  Google Scholar 

  20. Sun, Y., Dumont, C., Abidi, M.-A.M.: Mesh-based integration of range and color images. In: Society for Photo-Optical Instruments Engineering Conference Series, vol. 4051, pp. 110–117. (2000)

  21. Debevec, P., Hawkins, T., Tchou, C., Duiker, H.-P., Sarokin, W., Sagar, M.: Acquiring the reflectance field of a human face. In: Proceedings of 27th Annual Conference Computer Graph. Interaction Technology—SIGGRAPH’00, pp. 145–156. (2000)

  22. Lensch, H.P.A., Kautz, J., Goesele, M., Heidrich, W., Seidel, H.-P.: Image-based reconstruction of spatial appearance and geometric detail. ACM Trans. Graph. 22(2), 234–257 (2003)

    Article  Google Scholar 

  23. Debevec, P., Tchou, C., Gardner, A.: Estimating surface reflectance properties of a complex scene under captured natural illumination. In: USC ICT Technical Report, pp. 1–11. (2004)

  24. Troccoli, A., Allen, P.K.: Recovering illumination and texture using ratio images. In: Proceeding of Third International Symposium on 3D Data Process Visual Transmission 3DPVT 2006, vol. 1, pp. 655–662. (2007)

  25. Tan, R., Ikeuchi, K.: Separating reflection components of textured surfaces using a single image. Digit. Arch. Cult. Objects 27(2), 353–384 (2008)

    Article  Google Scholar 

  26. Osadchy, M., Jacobs, D., Ramamoorthi, R., Tucker, D.: Using specularities in comparing 3D models and 2D images. Comput. Vis. Image Underst. 111(3), 275–294 (2008)

    Article  Google Scholar 

  27. Lins, R.D., De França, G., Mariano, E., Fan, J., Majewicz, P., Thielo, M.: Removing shade and specular noise in images of objects and documents acquired with a 3D-scanner. pp. 299–307. (2013)

  28. Drauschke, M., Mayer, H.: The potential of specular reflections for façade image analysis. In: ISPRS Annual Photogrammetry Remote Sensing Spatial Information Sciences, vol. II-3/W4, pp. 33–40. (2015)

    Article  Google Scholar 

  29. Roth, S., Black, M.J.: “Fields of experts: a framework for learning image priors”, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2, 860–867 (2005)

    Google Scholar 

  30. “3D Visual Computing and Robotics Lab.” [Online]. Available: http://isa.esi.uclm.es/. Accessed 21 Aug 2018

  31. Bueno, G., Martínez-Albalá, A., Adán, A.: Fuzzy-Snake Segmentation of Anatomical Structures Applied to CT Images, pp. 33–42. Springer, Berlin (2004)

    Google Scholar 

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Acknowledgements

This work was supported by the Spanish Economy and Competitiveness Ministry (DPI2013-43344-R Project, AEI/FEDER, UE), by the Castilla–La Mancha Government (PEII-2014-017-P Project) and the 3A2400/NL38565 Grant.

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  1. 3D Visual Computing and Robotics Lab, Universidad de Castilla-La Mancha, Paseo de la Universidad, 4, 13071, Ciudad Real, Spain

    Samuel A. Prieto, Antonio Adán & Blanca Quintana

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  1. Samuel A. Prieto
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  2. Antonio Adán
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  3. Blanca Quintana
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Correspondence to Samuel A. Prieto.

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Prieto, S.A., Adán, A. & Quintana, B. Preparation and enhancement of 3D laser scanner data for realistic coloured BIM models. Vis Comput 36, 113–126 (2020). https://doi.org/10.1007/s00371-018-1584-9

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  • DOI: https://doi.org/10.1007/s00371-018-1584-9

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