Probabilistic Range Image Integration for DSM and True-Orthophoto Generation

  • Markus Rumpler
  • Andreas Wendel
  • Horst Bischof
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)

Abstract

Typical photogrammetric processing pipelines for digital surface model (DSM) generation perform aerial triangulation, dense image matching and a fusion step to integrate multiple depth estimates into a consistent 2.5D surface model. The integration is strongly influenced by the quality of the individual depth estimates, which need to be handled robustly. We propose a probabilistically motivated 3D filtering scheme for range image integration. Our approach avoids a discrete voxel sampling, is memory efficient and can easily be parallelized. Neighborhood information given by a Delaunay triangulation can be exploited for photometric refinement of the fused DSMs before rendering true-orthophotos from the obtained models. We compare our range image fusion approach quantitatively on ground truth data by a comparison with standard median fusion. We show that our approach can handle a large amount of outliers very robustly and is able to produce improved DSMs and true-orthophotos in a qualitative comparison with current state-of-the-art commercial aerial image processing software.

Keywords

Delaunay Triangulation Range Image Depth Estimate Aerial Image Neighborhood Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Collins, R.T.: A Space-Sweep Approach to True Multi-Image Matching. In: CVPR (1996)Google Scholar
  2. 2.
    Cramer, M.: The DGPF-Test on Digital Airborne Camera Evaluation – Overview and Test Design. Photogrammetrie - Fernerkundung - Geoinformation (2010)Google Scholar
  3. 3.
    Curless, B., Levoy, M.: A Volumetric Method for Building Complex Models from Range Images. In: SIGGRAPH (1996)Google Scholar
  4. 4.
    Fukunaga, K., Hostetler, L.D.: The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition. IEEE Transactions on Information Theory 21 (1975)Google Scholar
  5. 5.
    Gallup, D., Frahm, J.-M., Pollefeys, M.: A Heightmap Model for Efficient 3D Reconstruction from Street-Level Video. In: 3DPVT (2010)Google Scholar
  6. 6.
    Hilton, A., Stoddart, A.J., Illingworth, J., Windeatt, T.: Reliable Surface Reconstruction from Multiple Range Images. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, Springer, Heidelberg (1996)Google Scholar
  7. 7.
    Hirschmueller, H.: Accurate and efficient stereo processing by semi-global matching and mutual information. In: CVPR (2005)Google Scholar
  8. 8.
    Irschara, A., Rumpler, M., Meixner, P., Pock, T., Bischof, H.: Efficient and Globally Optimal Multi View Dense Matching for Aerial Images. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2012)Google Scholar
  9. 9.
    Kazhdan, M., Bolitho, M., Hoppe, H.: Poisson Surface Reconstruction. In: Symposium on Geometry Processing (2006)Google Scholar
  10. 10.
    Merrell, P., Akbarzadeh, A., Wang, L., Mordohai, P., Frahm, J.-M., Yang, R., Nistér, D., Pollefeys, M.: Real-time Visibility-based Fusion of Depth Maps. In: CVPR (2007)Google Scholar
  11. 11.
    Paparoditis, N., Thom, C., Jibrini, H.: Surface Reconstruction in Urban Areas from Multiple Views of Aerial Digital Frame Cameras. International Archives of Photogrammetry and Remote Sensing (2000)Google Scholar
  12. 12.
    Pix4D Aerial Image Processing Software (January 2013), http://www.pix4d.com
  13. 13.
    Rhemann, C., Hosni, A., Bleyer, M., Rother, V., Gelautz, M.: Fast Cost-Volume Filtering for Visual Correspondence and Beyond. In: CVPR (2011)Google Scholar
  14. 14.
    Scharstein, D., Szeliski, R.: A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. Int. Journal of Computer Vision 47 (2002)Google Scholar
  15. 15.
    Strecha, C., von Hansen, W., Van Gool, L., Fua, P., Thoennessen, U.: On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery. In: CVPR (2008)Google Scholar
  16. 16.
    Turk, G., Levoy, M.: Zippered Polygon Meshes from Range Images In: SIGGRAPH (1994)Google Scholar
  17. 17.
    Unger, C., Wahl, E., Sturm, P., Ilić, S.: Probabilistic Disparity Fusion for Real-Time Motion-Stereo. Technical Report, TUM (2010)Google Scholar
  18. 18.
    Vogiatzis, G., Torr, P., Cipolla, R.: Multi-View Stereo via Volumetric Graph-Cuts. In: CVPR (2005)Google Scholar
  19. 19.
    Wheeler, M., Sato, Y., Ikeuchi, K.: Consensus Surfaces for Modeling 3D Objects from Multiple Range Images. In: ICCV (1998)Google Scholar
  20. 20.
    Zach, C., Pock, T., Bischof, H.: A Globally Optimal Algorithm for Robust TV-L 1 Range Image Integration. In: ICCV (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Markus Rumpler
    • 1
  • Andreas Wendel
    • 1
  • Horst Bischof
    • 1
  1. 1.Institute for Computer Graphics and VisionGraz University of TechnologyAustria

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