HOW WELL PERFORMS FREE-FORM 3D OBJECT RECOGNITION FROM RANGE IMAGES?

This paper investigates the recognition performance of a geometric matching approach to the recognition of free-form objects obtained from range images. The heart of this approach is a closest point matching algorithm which, starting from an initial configuration of two rigid objects, iteratively finds their best correspondence. While the effective performance of this algorithm is known to depend largely on the chosen set of initial configurations, this paper investigates the quantitative nature of this dependence. In essence, we experimentally measure the range of successful configurations for a set of test objects and derive quantitative rules for the recognition strategy. These results show the conditions under which the closest point matching algorithm can be successfully applied to free-form 3D object recognition and help to design a reliable and cost-effective recognition system