Texture Generation and Mapping Using Video Sequences for 3D Building Models

  • Fuan Tsai
  • Cheng-Hsuan Chen
  • Jin-Kim Liu
  • Kuo-Hsing Hsiao
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Three-dimensional (3D) building model is one of the most important components in a cyber city implementation and application. This study developed an effective and highly automated system to generate and map (near) photo-realistic texture attributes onto 3D building models using digital video sequences. The system extracted frames with overlapped textures of building facades and integrated them to produce complete texture images. Interest points on the extracted video frames were identified using corner-detectors and matched with normalized cross-correlation for seamless stitching. Shadows and foreign objects were identified and removed with morphological algorithms and mended by mirroring neighborhood textures. Completed mosaicked texture images were mapped onto corresponding model facets by linear or parametric transformation. Test examples demonstrate that the developed system can effective generate seamless photo-realistic texture images and correctly map them onto complicated 3D building models with high efficiency.


texture mapping video mosaic 3D building model cyber city visualization 


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  1. [1]
    Rau, J-Y and L-C Chen, 2003, Robust reconstruction of building models from three-dimensional line segments. PE&RS, 69(2), pp. 181–188.Google Scholar
  2. [2]
    Vosselman, G. and S. Dijkman, 2001, 3D building model reconstruction from point clouds and ground plans. Int’l Archives of Photogrammetry and Remote Sensing, XXXIV-3/W4, pp. 37–43.Google Scholar
  3. [3]
    Chen, L-C, T-A Teo, J-Y Rau, J-K Liu and W-C Hsu, 2005, Building reconstruction from LIDAR data and aerial imagery. IGARS’05, 4, pp. 2846–2849.Google Scholar
  4. [4]
    Beck, M., 2003, Real-time visualization of big 3D city models. Int’l Archives of Photogrammetry and Remote Sensing, XXXIV-5/W10.Google Scholar
  5. [5]
    Coorg, S. and S. Teller, 2000, Spherical mosaics with quaternions and dense correlation. Int’l J. Computer Vision, 37(3), pp. 259–273.CrossRefGoogle Scholar
  6. [6]
    Gunadi, C. R., H. Shimizu, K. Kodama and K. Aizawa, 2002, Construction of large-scale virtual environment by fusing range data, texture images, and airborne altimetry data. 3DPVT’02, pp. 772–775.Google Scholar
  7. [7]
    Lee, S. C, S. K. Jung and R. Nevatia, 2002, Automatic integration of facade textures into 3D building models with projective geometry based line clustering. EUROGRAPHICS 2002, 21(3), pp. 259–273.Google Scholar
  8. [8]
    Tsai, F. H-C Lin, J-K Liu and K-H Hsiao, 2005, Semiautomatic texture generation and transformation for cyber city building models. IGARSS’05, 7, pp. 4980–4983.Google Scholar
  9. [9]
    Zheng, J.Y. And M, Shi, 2003, Mapping cityspaces to cyber space, CW2003, pp. 166–173.Google Scholar
  10. [10]
    Chon, J., T. Fuse and E. Shimizu, 2004, Urban visualization through video mosaics based on 3-D multibaselines. International Archives of Photogrammetry and Remote Sensing, XXXV-B3, pp. 727–731.Google Scholar
  11. [11]
    Gibson, S., B.J. Hubbold, J. Cook and T.L.J. Howard, 2003, Interactive reconstruction of virtual environments from video sequences. Computer & Graphics, 27(2), pp. 293–391.CrossRefGoogle Scholar
  12. [12]
    Nicolas, H., 2001, New methods for dynamic mosaicing. IEEE Transactions on Image Processing, 10(8), pp. 1239–1251.CrossRefGoogle Scholar
  13. [13]
    Spann, J.R. And K.S. Kaufman, 2000, Photogrammetry using 3D graphics and projective textures. IAPAS, Amsterdam, vol. 33.Google Scholar
  14. [14]
    Guillou, E., D. Meneveaux, E. Maisel and K. Bouatouch, 2000, Using vanishing points for camera calibration and coarse 3D reconstruction from a single image. The Visual Computer, 16, pp. 396–410.CrossRefGoogle Scholar
  15. [15]
    Kumar, R, H.S. Sawhney, Y. Guo, S. Hsu and S. Samarasekera, 2000, 3D manipulation of motion imagery. Proc. ICIP 2000, vol. 1, pp. 17–20.Google Scholar
  16. [16]
    Kim, D.H., Y.I. Yoon and J.S. Choi, 2003, An efficient method to build panoramic image mosaics. Pattern Recognition Letters, 24, pp. 2421–2429.]CrossRefGoogle Scholar
  17. [17]
    Du, Y., J. Cihlar, J. Beaubien and R. Latifovic, 2001, Radiometric normalization, composition, and quality control for satellite high resolution image mosaics over large area. IEEE TGARS, 39(3), pp. 623–634.Google Scholar
  18. [18]
    Uyttendaele, M., A. Eden and R. Szeliski, 2001, Eliminating ghosting and exposure artifacts in image mosaics. CVPR 2001, 2, pp. 509–516.Google Scholar
  19. [19]
    Adelson, E.H., C.H. Anderson, J.R. Bergen, P.J. Burt and J.M. Ogden, 1984, Pyramid method in image process, RCA Engineer, 29(6), pp. 33–41.Google Scholar
  20. [20]
    Levin, A., A. Zomet. S. Peleg and Y. Weiss, 2004, Seamless Image Stitching in the Gradient Domain. ECCV 2004, LNCS 3024, pp. 377–389.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fuan Tsai
    • 1
  • Cheng-Hsuan Chen
    • 1
  • Jin-Kim Liu
    • 2
  • Kuo-Hsing Hsiao
    • 2
  1. 1.Center for Space and Remote Sensing ResearchNational Central UniversityZhong-LiTaiwan
  2. 2.Energy and Resources LaboratoryIndustrial and Technology Research InstituteHsin-ChuTaiwan

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