Three dimensional reconstruction of cylindrical pellet using stereo vision

In the present work, we propose a framework for retrieving metric information of featureless cylindrical pellet imaged through stereo vision. Considering the isometry property, relative affine structure which is an invariant that depends on the depth of the point is employed for obtaining the information. This method skips considering vanishing points for depth estimation, and hence the errors are reduced remarkably as no points are assumed to be at infinity. Real world measurements are taken and the metrics of a selected reference point is assumed to be known. With these minimal information the mapping between the images using the relative affine structure is carried out that enables the 3D reconstruction. This work also blends the concepts of 360 degree rotational symmetry and orthogonal planes with the stereo vision that has minimized the error percentage. The results presented by the theory will have an impact on the design of 3D reconstruction systems for computer vision and its applications.