AUTOMATIC 3D BUILDING MODEL GENERATION USING A HYBRID APPROACH

Accurate and up-to-date 3D building models are quite valuable for several applications such as city planning, disaster management, and military simulations. As location-based services and personal navigation become more accessible to the public, automated and efficiently generated 3D models are required more urgently than ever. Considering the importance of 3D building models, they still lack economic and reliable techniques for their generation while taking advantage of the available multi-sensory data from single and multiple platforms. The research conducted on 3D building model generation may fall into the following three categories: data sources used (single or multi-source approaches), the processing strategy (data-driven or model-driven), and the amount of user interaction (semiautomatic or fully automatic). The objectives of this research is to propose fully-automatic building generation approach by integrating data-driven and model-driven methods while making use of multiple images and LiDAR datasets. The focus of reconstruction is on complex structures, which comprise a collection of rectangular primitives. The proposed methodology generates building hypotheses and initial-boundaries from LiDAR data (i.e., data-driven method) and this information is used to restrict the search space and to resolve the matching ambiguities in the images’ space (i.e., model-based image fitting). To reduce the number of involved models, rectangular primitives are used and model parameters, height and slopes are determined from LiDAR data. An automatic algorithm for decomposing the initial LiDAR boundaries into several rectangular primitives is introduced.