Recovering the three-dimensional (3D) object shape remains an unresolved area of research on the cross-section of computer vision, photogrammetry and bioinformatics. Although various techniques have been developed, the computational complexity and the constraints introduced to overcome the problems have limited their applicability in the real world scenarios. In this paper, we propose a method that is based on the projective geometry between the object space and the silhouette-images taken from multiple view-points. The approach eliminates the problems related to dense feature point matching and camera calibration that are generally adopted by many state of the art shape reconstruction methods. The object shape is reconstructed by establishing a set of hypothetical planes slicing the object volume and estimating the projective geometric relations between the images of these planes. The experimental results show that the 3D object shape can be recovered by applying minimal constraints.