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)


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.


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.


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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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