Model acquisition of 3D objects based on clustering by agents

In this paper, we propose a method for acquiring shape models of 3D objects from range data of objects in a class. Since objects in a class have various kinds of structures, a shape model is generated for every structure. First, a range image is segmented into parts based on the curvature of a surface, and the part is approximated as superquadrics. Superquadrics are parametric representation for a 3D shape; their parameters can be criterion for similarity between 3D shapes. Next, integration of parts is required because the segmentation based on curvature makes excessive parts. For this purpose, "Part agent" is assigned to the part, and integrates parts by interaction based on a fitting error. From the results, input objects are clustered into groups. This clustering method is based on interaction by "Object agent", which is assigned to the object. First, the object agents in charge of same structured objects make a group. An object agent evaluates groups based on an evaluation function, then move to the group with highest evaluation. The evaluation function is based on the fitting error in the case of belonging to the group, the distance from the average shape of the group, the scale of the group, and the number of parts. A clustering result is obtained when the agents do not move. Since each cluster includes only same structured objects, the object in the cluster can be represented as the same number of parameters. Therefore, a shape model is generated by manipulating parameters. Finally, the experimental results show that the proposed method can acquired valid shape models and can improve segmentation results.