An Automatic Comparison Approach to Detect Errors on 3D City Models

Benjamin Gorszczyk 1 Guillaume Damiand 1 Sylvie Servigne 2 Abdoulaye Abou Diakité 3 Gilles Gesquière 4
1 M2DisCo - Geometry Processing and Constrained Optimization
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 BD - Base de Données
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
3 3D GeoInformation, Delft University of Technology
4 GeoMod - Modélisation Géométrique, Géométrie Algorithmique, Fractales
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : 3D building models are needed in several professional domains. To provide better results, these models must be errors-free and that is why it is required to have a way to detect and to correct errors. These errors can be geometric, topological or semantic. By using a topological structure called EBM-LCC that allows to model buildings, we create a new tool that allows to detect these three type of errors in 3D city models. The solution we propose is an algorithm that compares two EBM-LCC. This algorithm can be used to compare two different models, for example acquired with two different processes, or resulting from two different acquisition campaigns. It is also an interesting tool to compare and validate algorithms. In this work, we compare an EBM-LCC loaded directly from a CityGML model with an EBM-LCC reconstructed from a soup of polygons only. Then we can use the result of this comparison to outline possible differences or to correct one of the two models by using the information of the other one. This algorithm allowed to automatically detect and correct semantic errors on several models that are currently used by professionals. This shows the interest of EBM-LCC for the city modeling domain as it helps to reach an error-free model.
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Benjamin Gorszczyk, Guillaume Damiand, Sylvie Servigne, Abdoulaye Abou Diakité, Gilles Gesquière. An Automatic Comparison Approach to Detect Errors on 3D City Models. Eurographics Workshop on Urban Data Modelling and Visualisation, UDMV2016, Dec 2016, Liège, Belgium. pp.25-30, ⟨10.2312/udmv.20161425⟩. ⟨hal-01458396⟩

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