High-speed 3D object recognition using additive features in a linear subspace

In this paper we propose a method of high-speed 3D object recognition using linear subspace method and our 3D features. This method can be applied to partial models with any size in any posture. Although it is becoming easy to obtain textured 3D models by a 3D scanner, there are few methods for 3D object recognition which take into account both shape and textures of objects. Moreover, it is difficult to achieve high-speed processing of large 3D data. Our 3D features consider the co-occurrence of shape and colors of an object's surface. The additive property of these features makes it possible to calculate the similarity between a query part and the subspace of each object in a database without division, and therefore the time for recognition is quite short. In the experiments, we compare our method with conventional methods using Spin-Images and Textured Spin-Images. We show that our method is appropriate for 3D object recognition.