3D object retrieval based on sparse coding in weak supervision

• The proposed method does not require the explicit virtual model information.
• We utilized FDDL to learn dictionary to reconstruct query sample for retrieval.
• Our approach has high generalization ability and can be used on other applications.

With the rapid development of computer vision and digital capture equipment, we can easily record the 3D information of objects. In the recent years, more and more 3D data are generated, which makes it desirable to develop effective 3D retrieval algorithms. In this paper, we apply the sparse coding method in a weakly supervision manner to address 3D model retrieval. First, each 3D object, which is represented by a set of 2D images, is used to learn dictionary. Then, sparse coding is used to compute the reconstruction residual for each query object. Finally, the residual between the query model and the candidate model is used for 3D model retrieval. In the experiment, ETH, NTU and ALOL dataset are used to evaluate the performance of the proposed method. The results demonstrate the superiority of the proposed method.