We address the recovery of segmented, 3-D descriptions of an object from intensity images. We use three views of an object from slightly different viewpoints as our input. For each image we extract a hierarchy of groups based on proximity, parallelism and symmetry in a robust manner. The groups in the three images are matched by computing the epipolar geometry. For each set of matched groups from the three images, we then label the contours of the groups as "true" or "limb" edges. Using the information about groups, the label associated with their contours and projective properties of subclasses of Generalized Cylinders, we infer the 3-D structure of these groups. The proposed method not only allows robust shape recovery but also produces segmented parts. Our approach can also deal with groups generated as a result of texture or shadows on the object. We present results on real images of moderately complex objects.