Least-squares model-image fitting of floating models for building extraction from images

Model-based building extraction (MBBE) from images has received intensive research interest in the field of digital photogrammetry for the last decade. Model description and a fitting algorithm are the primary issues addressed in this paper. This paper proposes a novel approach, called the floating model, to modeling a variety of buildings by fitting primitive models onto images. Each building is represented by a combination of 3D primitive models, and each primitive model is associated with a set of shape and pose parameters. Building extraction is carried out by adjusting these model parameters until the projection of the model fits onto all images. A semi-automated strategy is proposed to increase the efficiency of the adjustments made to the model. First, the model is manually dragged and dropped to the approximate shape and position of all images. Then, the optimal fit is automatically computed by means of the tailored Least-squares Model-image Fitting (LSMIF) algorithm, which is the focus of this paper. The accuracy of the algorithm is assessed, and additional constraints to ensure its robustness are introduced. Finally, the algorithm is tested on real datasets and is compared with manually measured data to assess its empirical accuracy. The results reveal that the function of LSMIF is stable and can generate satisfying 3D information on a building comparable to manually measured data.