Active shape inferring based on the symmetry in stable poses-shape from function approach

Automatic acquisition of a three-dimensional object model is one of major goals of computer vision research. We develop a new algorithm for reconstructing the 3D shapes of objects from multiview images which are automatically acquired based on the symmetry in stable poses. Our approach is based on a “shape from function” strategy. That is, shape is inferred by observing and estimating “the stability in stable poses”, which is the most fundamental condition for objects to be functioning in the real world. Based on that assumption, we first observe the object from the vertical top and estimate a plane of symmetry using the center of gravity and the symmetrical axis of the silhouette in the top view image. We then estimate the next viewpoints, from which the estimated symmetry are examined with silhouettes obtained as projections on the estimated plane of symmetry. By executing this symmetry estimation-examination process recursively, we reconstruct the 3D shape of the object as a product of straight homogeneous generalized cylinders (SHGCs) whose cross section and the axis direction are constrained from the silhouette at each view-point and the viewing direction respectively by using octree representation or a CAD system. Experimental results show that the effectiveness of the approach in reconstructing the 3D shape of object