A Featureless Approach to 3D Polyhedral Building Modeling from Aerial Images

This paper presents a model-based approach for reconstructing 3D polyhedral building models from aerial images. The proposed approach exploits some geometric and photometric properties resulting from the perspective projection of planar structures. Data are provided by calibrated aerial images. The novelty of the approach lies in its featurelessness and in its use of direct optimization based on image rawbrightness. The proposed framework avoids feature extraction and matching. The 3D polyhedral model is directly estimated by optimizing an objective function that combines an image-based dissimilarity measure and a gradient score over several aerial images. The optimization process is carried out by the Differential Evolution algorithm. The proposed approach is intended to provide more accurate 3D reconstruction than feature-based approaches. Fast 3D model rectification and updating can take advantage of the proposed method. Several results and evaluations of performance from real and synthetic images show the feasibility and robustness of the proposed approach

3D building reconstruction by map based generation and evaluation of hypotheses

This paper presents a knowledge-based approach for automatic 3D reconstruction of buildings from aerial images. By combining the image analysis with information from GIS maps and specific knowledge of the buildings the complexity of the building reconstruction task can be greatly reduced. The building reconstruction process is described as a tree search in the space of possible building hypotheses. Hypotheses derived from outlines of building footprints from the map are fit against image pixel gradients. To guide the search of the tree an evaluation function based on information theory principles is defined. The proposed evaluation function defines the score of matching between a hypothesised building model and the image pixel gradients. It uses a mutual information measure and MDL criterion to select the best fit to image data in the tree search.