High-speed object pose recognition using distinctive 3-D vector pairs

We propose a high-speed 3-D object pose recognition method. The method's main feature is that a set of distinctive 3-D vector pairs, each of which consists of three different 3-D points, is used for matching process. A vector pair with a low occurrence probability of a model object means that it is distinctive not only in a model but also in an acquired image. Therefore, such vector pairs are expected to avoid false matching. Experimental results have shown that the proposed method is about 60 times faster and increases the recognition success rate from 62.0% to 94.6% in comparison with the Spin Image method.