Appearance based pose estimation of 3D object using support vector regression

Several methods for estimating the pose of a 3D object from its appearance have been proposed. The parametric eigenspace method is typical of such methods. One key disadvantage of this method is that storage requirements explode when the degree of freedom is increased. In this paper, we propose a method of suppressing this increase in storage requirements by describing the relationship between an image and a pose as functions. Pose estimation functions, which keep the generalization ability high even if the storage requirements are small, are obtained by using support vector regression. Experimental results show that the proposed method can compress the storage requirements to just 1/100 of that needed by the parametric eigenspace method.