Skip to main content
SpringerLink
Log in

A System for 3D Modeling Frescoed Historical Buildings with Multispectral Texture Information

  • Special Issue
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

This work proposes a system for the automatic construction of multi-spectral three-dimensional (3D) models of architecture. Besides the specific application, which concerns the interactive visualization and the restoration of historical buildings, the interest of the proposed system lies in the instrumental gap it fills in the multi-spectral nature of the textures, in general needed for rendering with faithful colors, and in the automatism of the 3D model construction. The paper presents a robust procedure for matching 3D points of architecture scenes and a new multiresolution method for texture generation. The proposed system is an effective tool for producing 3D content amenable to a great number of usages.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. RIEGL + LMS-Z420i http://www.riegl.com/

  2. CYRAX2500 http://www.cyra.com/news/cyrax2500.html

  3. Dias, P., Sequeira, V., Vaz, F., Goncalves J.G.M.: Fusion of intensity and range data for improved 3D models. In: International Conference on Image Processing ICIP01, Thessaloniki, Greece. IEEE Press (2001)

  4. Dias, P., Sequeira, V., Vaz, F., Goncalves, J.G.M.: Registration and fusion of intensity and range data for 3D modelling of real world scenes. In: Fourth international conference on 3-D digital imaging and modeling 3DIM2003, Banff, Canada. IEEE Press (2003)

  5. Bostrom, G., Fiocco, M., Puig, D., Rossini, A., Goncalves, J.G.M. Sequeira, V.: Acquisition, modelling and rendering of very large urban environments. In: Second international symposium on 3D data processing, visualization and transmission 3DPVT04, Thessaloniki, Greece. IEEE Press (2004)

  6. http://www.eyetronics.com/, Eyetronics: 3D scanning services and hardware

  7. http://www.cyberware.com/, Cyberware: 3D scanning systems and software

  8. http://www.konicaminolta 3d.com/, Konica Minolta: 3D scanning systems

  9. Bosch, T., Lescure, M.: Selected papers on laser distance measurement, SPIE Milestone Series, MS115, Bellingham, WA, 1995

  10. Dorsch R.G., Hausler G., Hermann J.M.(1994): Laser triangulation: Foundamental uncertainty in distance measurement. Appl. Optics 33: 1306–1314

    Article  Google Scholar 

  11. Donati S.(2004): Electro-optical instrumentation. Prentice Hall, Englewood Cliffs

    Google Scholar 

  12. Martinez K., Cupitt J., Saunders D., Pillay R.(2002): Ten years of art imaging research. Proc. IEEE 90(1): 28–41

    Article  Google Scholar 

  13. Sharma G., Vhrel M.J., Trussell H.J.(1998): Color imaging for multimedia. Proc. IEEE 86(6): 1088–1108

    Article  Google Scholar 

  14. Sharma G., Trussell H.J.(1997): Digital color imaging. IEEE Trans. Image Process. 6(7): 901–932

    Article  Google Scholar 

  15. Stratoudaki T., Xenakis D., Zafiropulos V., Anglos D.(2000): Laser induced breakdown spectroscopy in the analysis of pigments in painted artworks a database of pigments and spectra. Optics Lasers Biomed. Cult. Ser. Int. Soc. Optics Life Sci. 5: 163–168

    Google Scholar 

  16. Anglos D.(2001). Laser-induced breakdown spectroscopy in art and archaeology. Appl Spectrosc. 55:186A–205A

    Article  Google Scholar 

  17. Martinez K., Hamber A.(1989): Towards a colorimetric digital image archive for the visual arts. Proc. IEEE 1073: 114–121

    Google Scholar 

  18. Martinez K.(1991): High resolution digital imaging of paintings: the vasari project. Microcomput. Inf. Manage. 8(4): 277–283

    Google Scholar 

  19. Martinez K., Cupitt J., Saunders D.(1993): High resolution colorimetric imaging of paintings. Proc. SPIE Cameras Scanners Image Acquisition Syst. 1901: 25–36

    Google Scholar 

  20. Martinez K.(1996): High quality digital imaging of art in europe. Proc. SPIE Very High Resolut. Qual. Imag., 2663: 69–75

    Google Scholar 

  21. Cupitt J., Martinez K., Saunders D.(1996): Methodology for art reproduction in colour: the MARC project. Comput. Hist. Art J. 6(2): 1–20

    Google Scholar 

  22. Farrell, J.E., Cupitt, J., Saunders, D., Wandell, B.A.: Estimating spectral reflectances of digital artwork. In: Proceedings of the international symposium on multispectral imaging and color reproduction for digital archival, pp. 58–64 (1999)

  23. Schmitt F., Brettel H., Hardeberg J.Y.(2000): Multispectral imaging development at ENST. Displaying Imaging 8: 261–268

    Google Scholar 

  24. Maître H., Schmitt F., Crettez J.-P.(1997): High quality imaging in museum: from theory to practice. Proc. SPIE - Very High Resolut. Qual. Imaging II 3025: 3039

    Google Scholar 

  25. Maître, H., Schmitt, F., Lahanier, C.: 15 years of image processing and the fine arts. In: Proceedings of the IEEE International conference on image processing (ICIP’01), Thessaloniki, Greece, October 2001, vol.1, pp. 557–561 (2001)

  26. Schmitt, F.: High quality digital color images. In: Proceedings of the international conference on high technology, (CHIBA’96), pp. 55–62 (1996)

  27. Maître, H., Schmitt, F., Crettez, J.P., Wu, Y., Hardeberg, J.Y.: Spectrophotometric image analysis of fine art paintings. In: Proceedings of the 4th colour imaging conference, IS&T - SID, Scottsdale, Arizona, November 1996, pp. 50–53 (1996)

  28. Hardeberg, J.Y., Schmitt, F., Brettel, H., Crettez, J.-P., Maître, H.: Multispectral image acquisition and simulation of illuminant changes. In: Colour imaging: vision and technology, pp. 145–164 (1999)

  29. Hardeberg, J.Y., Schmitt, F., Brettel, H.: Multispectral image capture using a tunable filter. In: Proceedings of Color imaging: device independent color, color hardcopy and graphic arts V, vol. 3963, SPIE Proceedings, pp. 77–88 (2000)

  30. Hardeberg, J.Y.: Multispectral color image acquisition. In: Proceedings of NORSIG-2001, Trondheim, Norway, October 2001, pp. 77–82 (2001)

  31. Hardeberg, J.Y.: Acquisition and reproduction of color images – colorimetric and multispectral approaches. Dissertation.com, USA (2001)

  32. Yokoyama, Y., Tsumura, N., Haneishi, H., Miyake, Y., Hayashi, J., Saito, M.: A new color management system based on human perception and its application to recording and reproduction of art paintings. In: Proceedings of the 5th color imaging conference: color science, systems, and applications, pp. 169–172 (1997)

  33. Haneishi H., Hasegawa T., Hosoi A., Yokoyama Y., Tsumura N., Miyake Y.(2000): System design for accurately estimating spectral reflectance of art paintings. Appl. Optics 39(35): 6621–6632

    Article  Google Scholar 

  34. Haneishi, H., Hasegawa, T., Tsumura, N., Miyake, Y.: Design of color filters for recording artworks. In: Proceedings of the IS&T 50th annual conference, pp. 369–372 (1997)

  35. Cotte, P., Dupouy, M.: Crisatel high resolution multispectral system. In: PICS 2003: the PICS conference, an international technical conference on the science and systems of digital photography, science, Rochester, NY, May 2003, IEEE Press (2003)

  36. Wandell B.A.(1995): Foundations of vision. Sinauer, Sunderland

    Google Scholar 

  37. Vhrel M.J., Gershon R., Iwan L.S.(1994): Measurement and analysis of obejct reflectance spectra. Color Res. Appl. 19(1): 4–9

    Google Scholar 

  38. http://musis.forth-photonics.gr/

  39. Vhrel M.J., Trussell H.J.(1994): Filter considerations in color correction. IEEE Trans. Image Process. 3(4): 147–161

    Google Scholar 

  40. Vhrel M.J., Trussell H.J.(1997): Mathematical methods for the design of color scanning filter. IEEE Trans. Image Process. 6(2): 312–320

    Article  Google Scholar 

  41. Sharma G., Trussell H.J., Vhrel M.J. (1998): Optimal nonnegative color scanning filters. IEEE Trans. Image Process. 7(1): 129–133

    Article  Google Scholar 

  42. Koenig F., Praefke W.(1998): The practice of multispectral image acquisition. Proc. SPIE Electr. Imaging Process. Print. Publ. Color 3409: 34–41

    Google Scholar 

  43. Poger, S., Angelopoulou, E.: Selecting components for building multispectral sensors. In: IEEE CVPR technical sketches (CVPR tech sketches), December 2001.

  44. Imai, F.H., Berns, R.S.: Spectral estimation using trichromatic digital cameras. In: Proceeding of the international symposium on multispectral imaging and color reproduction for digital archives, pp. 42–49 (1999)

  45. Imai, F.H.: Multi-spectral image acquisition and spectral reconstruction using a trichromatic digital camera system associated with absorption filters. Munsell Color Science Laboratory Technical Report

  46. Imai, F.H., Berns, R.S.: A comparative analysis of spectral reflectance reconstruction in various spaces using a trichromatic camera system. In Proceedings of IS&T/SID 7th color imaging conference, pp. 21–25 (1999)

  47. Berns R.S., Imai F.H., Burns P.D., Tzeng D.-Y.(1998): Multi-spectral-based color reproduction research at the munsell color science laboratory. Proc. SPIE Electr. Imaging Process. Print. Publ. Color 3409: 14–25

    Google Scholar 

  48. Imai, F.H., Berns, R.S.: High-resolution multispectral image archives – a hybrid approach. In: Proceedings of the 6th IS&T/SID color imaging conference: color science, systems, and applications, pp. 224–227 (1998)

  49. Imai, F.H., Rosen, M.R., Berns, R.S.: Comparison of spectrally narrow-band capture versus wide-band with a priori sample analysis for spectral reflectance estimation. In: Proceedings of the 7th color imaging conference: color science, systems, and applications: putting it all together (2000) (in press)

  50. Wolfe, W.L.: Introduction to imaging spectrometers. SPIE tutorial text, vol. 25 (1997)

  51. http://www.specim.fi

  52. Rueger J.M.(1996): Electronic distance measurement. Springer, Berlin Heidelberg New York

    Google Scholar 

  53. http://www.dimetix.com/downloads/ManualDLSA.pdf, Distance laser sensor

  54. Andreetto M., Brusco N., Cortelazzo G.M.(2004): Automatic 3D modeling of textured cultural heritage objects. IEEE Trans. Image Process. 13(3): 354–369

    Article  Google Scholar 

  55. Brusco, N., Giorgi, A., Andreetto, M., Cortelazzo, G.M.: 3d-registration by textured spin-images. In: Proceedings of the 5th international conference on 3-D digital imaging and modeling (3DIM05), Banff, Alberta, Canada, June 2005, pp. 592–599, IEEE Press (2005)

  56. Lucchese L., Doretto G., Cortelazzo G.M.(2002): A frequency domain technique for 3-D view registration. IEEE Trans. Pattern Anal. Mach. Intell. 24(11): 1468–1484

    Article  Google Scholar 

  57. Pulli, K.: Multiview registration for large data sets. In: Proceedings of the 2nd international conference on 3-D digital imaging and modeling, Ottawa, Canada, October 1999, pp. 113–120 (1999)

  58. Wheeler, M., Sato, Y., Ikeuchi, K.: Consensus surfaces for modeling 3D objects from multiple range images. In: Proceedings of 6th international conference on computer vision, pp. 917–924, IEEE (1998)

  59. Ashbrook A., Fisher R., Robertson C., Werghi N.(1998): Finding surface correspondence for object recognition and registration using pairwise geometric histograms. Proc. Eur. Conf. Comput. Vision 1407: 674–786

    Google Scholar 

  60. Dorai C., Wang G., Jain A., Mercer C.(1998): Registration and integration of multiple object views for 3D model construction. IEEE Trans. Pattern Anal. Mach. Intell 20(1): 83–89

    Article  Google Scholar 

  61. Higuchi K., Hebert M., Ikeuchi K.(1995): Building 3D models from unregistered range images. Graph. Models Imag. Proc. 57(4): 315–333

    Article  Google Scholar 

  62. Huber, D., Hebert, M.: Fully automatic registration of multiple 3D data sets. IEEE workshop on computer vision beyond the visible spectrum, pp. 433–449 (2001)

  63. Zhang, D., Hebert, M.: Harmonic maps and their applications in surface matching. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR 99), November 1999, pp. 524–530 (1999)

  64. Johnson, A.E.: Spin-images: a representation for 3-D surface matching. Ph.D. thesis, Carnegie Mellon University, Pittsburgh, August 1997

  65. Johnson A.E., Hebert M. (1999): Using spin-images for efficient multiple model recognition in cluttered 3-D scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21(5): 433–449

    Article  Google Scholar 

  66. Krsek, P., Pajdla, T., Hlavac, V., Martin, R.: Range image registration driven by hierarchy of surface differential features. In: Proceedings of the 22nd Workshop of the Austrian association for pattern recognition, May 1998, pp. 175–183 (1998)

  67. Vanden Wyngaerd J., Van Gool L. (2002): Coarse registration of surface patches with local symmetries. Proc. Eur. Conf. Comput. Vision (ECCV’02) 2351: 572–586

    Google Scholar 

  68. Vanden Wyngaerd, J., Van Gool, L., Koch, R., Proesmans, M.: Invariant-based registration of surface patches. In: Proceedings of the international conference on computer vision (ICCV’99), pp. 301–306 (1999)

  69. Van Gool, L., Vandermeulen, D., Kalberer, G., Tuytelaars, T., Zalesny, A.: Modeling shapes and textures from images: new frontiers. In: Proceedings of 1st international symposium on 3D data processing visualization and transmission (3DPVT2002), Padova, Italy, June 2002, pp. 286–294, IEEE Press (2002)

  70. Roth, G.: Registering two overlapping range images. In: Proceeding of the 2nd international Conference on 3D digital imaging and modeling, Ottawa, Canada, October 1999, pp. 191–200 (1999)

  71. Gat, N.: Principal components transformation for real-time hyperspectral data compression. Opto-Knowledge Systems, Inc. Report AFRL-SN-WP-TR-2001-1171, October 2001

  72. Zanuttigh, P., Brusco, N., Taubman, D., Cortelazzo, G.M.: Greedy non-linear optimization of the plenoptic function for interactive transmission of 3D scenes. In: International conference of image processing, ICIP05, Genova, Italy, September 2005

  73. Taubman D.T., Marcellin M.W.(2002): Image compression fundamentals standards and practice. Kluwer, Dordrecht

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131, Padova, Italy

    N. Brusco, S. Capeleto, M. Fedel, A. Paviotti, L. Poletto, G. M. Cortelazzo & G. Tondello

  2. Istituto Nazionale di Fisica della Materia – CNR, University of Padova, Via Gradenigo 6/B, 35131, Padova, Italy

    S. Capeleto, M. Fedel, L. Poletto & G. Tondello

Authors
  1. N. Brusco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Capeleto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Fedel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Paviotti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. L. Poletto
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. G. M. Cortelazzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. G. Tondello
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. M. Cortelazzo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Brusco, N., Capeleto, S., Fedel, M. et al. A System for 3D Modeling Frescoed Historical Buildings with Multispectral Texture Information. Machine Vision and Applications 17, 373–393 (2006). https://doi.org/10.1007/s00138-006-0026-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-006-0026-2

Keywords

Search

Navigation