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We propose a high-speed 3-D object detection method that can recognize the position and pose of objects in complicated scenes consisting of randomly stacked objects. 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 objects with an acquired range image. Such distinctive vector pairs represent the local shape of an object, and are extracted by calculating the occurrence probability of each 3-D vector pair in a model object. A vector pair with a low occurrence probability means that it is distinctive not only in a model but also in an acquired image. Therefore, the method is expected to avoid false matching even if there are similar objects around the target object. It also substantially reduces processing time because the number of vector pairs is much smaller than all the data points of the object model. Experiments confirm that in comparison with the Spin Image method, the proposed method is about 60 times faster and increases the recognition success rate from 62.0% to 94.6%.