Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W2, 257-261, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W2-257-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
16 Aug 2013
STOCHASTIC REASONING FOR UAV SUPPORTED RECONSTRUCTION OF 3D BUILDING MODELS
S. Loch-Dehbi, Y. Dehbi, and L. Plümer Institute of Geodesy and Geoinformation, University of Bonn, Meckenheimer Allee 174, Bonn, Germany
Keywords: Stochastic Reasoning, Gaussian Mixture Models, Constraint Propagation, Bayesian Networks, Symmetry, UAV, 3D Building Models Abstract. The acquisition of detailed information for buildings and their components becomes more and more important. However, an automatic reconstruction needs high-resolution measurements. Such features can be derived from images or 3D laserscans that are e.g. taken by unmanned aerial vehicles (UAV). Since this data is not always available or not measurable at the first for example due to occlusions we developed a reasoning approach that is based on sparse observations. It benefits from an extensive prior knowledge of probability density distributions and functional dependencies and allows for the incorporation of further structural characteristics such as symmetries. Bayesian networks are used to determine posterior beliefs. Stochastic reasoning is complex since the problem is characterized by a mixture of discrete and continuous parameters that are in turn correlated by nonlinear constraints. To cope with this kind of complexity, the implemented reasoner combines statistical methods with constraint propagation. It generates a limited number of hypotheses in a model-based top-down approach. It predicts substructures in building facades – such as windows – that can be used for specific UAV navigations for further measurements.
Conference paper (PDF, 3155 KB)


Citation: Loch-Dehbi, S., Dehbi, Y., and Plümer, L.: STOCHASTIC REASONING FOR UAV SUPPORTED RECONSTRUCTION OF 3D BUILDING MODELS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W2, 257-261, https://doi.org/10.5194/isprsarchives-XL-1-W2-257-2013, 2013.

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