A variational approach for 3D object classification with retrieval of missing data

Abstract

In this paper, we propose a classification method for single views of 3D objects with missing data retrieval. A mobile robot equipped with range sensors basically obtains only single view information of a 3D scene. Therefore, large amount of information is missing by self-occlusion, which leads to severe restriction on the object classification exploiting the whole shapes of 3D objects. Humans can precisely identify the objects from single view, since they already have concepts of the entire shape of 3D objects by learning process. Based on these concepts, humans can infer the entire shape and category of the object from a single view. Inspired from this, the proposed algorithm learns concepts in abbreviated form for the shapes of 3D objects, then infers the entire shape and object category from these concepts simultaneously. We apply a generative model based on variational auto-encoder (VAE) to learn the concepts for complex shapes of 3D objects. Our method is evaluated on 3D CAD model dataset, and also compared with other state-of-the-art methods.