Abstract
3D representations have been recognized as an essential component of Augmented Reality (AR) oriented applications. However, not many examples of AR-oriented applications employ structured 3D data models despite the existence of standard 3D information models like CityGML. One of the reasons for this shortcoming can be explained by lack of a semantic-based modeling method for enriching AR-oriented data models with 3D features. Therefore, a three step procedure is proposed to address this limitation as (1) back-ward engineering of an AR-oriented application to its current data model, (2) enriching the current data model with 3D features, (3) and mapping the enriched model to a standard 3D information model. A notable contribution of this work is that the procedure of data modeling has been subject to the UModelAR meta-model which has brought a complementary standpoint to the employment of 3D geospatial modeling in AR environments. Furthermore, the 3D enriched data model has been mapped to CityGML information model with CityGML Application Domain Extension (ADE) concept. To demonstrate the feasibility of this approach, an operating mobile AR-oriented game has been used for the case study.
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Acknowledgments
This study was done part of GeoEduc3D project, a research initiative funded by the Canadian Network of Excellence—GEOIDE (GEOmatics for Informed Decisions). Accordingly we wish to thank all scientific and industrial partners, the Geomatics Department and Centre de Recherche en Géomatique (CRG) at Laval University for their supports. We also thank Thomas Butzbach from the developers of the Energy War mobile game for helping us with procedure of back-ward-engineering.
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Zamyadi, A., Pouliot, J., Bédard, Y. (2013). A Three Step Procedure to Enrich Augmented Reality Games with CityGML 3D Semantic Modeling. In: Pouliot, J., Daniel, S., Hubert, F., Zamyadi, A. (eds) Progress and New Trends in 3D Geoinformation Sciences. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29793-9_15
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