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PROJECTIVE RECONSTRUCTION OF BUILDING SHAPE FROM SILHOUETTE IMAGES ACQUIRED FROM UNCALIBRATED CAMERAS

Recovering the three-dimensional (3D) object shape lies as an unresolved and active research topic on the cross-section of computer vision, photogrammetry and bioinformatics. Although various techniques have been developed to tackle the shape recovery problems, the computational complexity and the constraints introduced by the other algorithms have limited the applicability of these methods in real world problems. In this paper, we propose a method that is based on the projective geometry between the object space and silhouette images taken from multiple viewing angles. The approach eliminates the requirements of dense feature matching and camera calibration that are generally adopted by other reconstruction method. The object is reconstructed by setting a set of hypothetical planes slicing the object volume and estimating the projective geometric relations between the images. The experimental results show that satisfactory 3D model can be generated by applying minimal constraints.