Multi-View Hierarchical Fusion Network for 3D Object Retrieval and Classification

The rapid development of 3D technique has led to the dramatic increase in 3D data. The scalable and effective 3D object retrieval and classification algorithms become mandatory for large-scale 3D object management. One critical problem of view-based 3D object retrieval and classification is how to exploit the relevance and discrimination among multiple views. In this paper, we propose a multi-view hierarchical fusion network (MVHFN) for these two tasks. This method mainly contains two key modules. First, the module of visual feature learning applies the 2D CNNs to extract the visual feature of multiple views rendered around the specific 3D object. Then, the multi-view hierarchical fusion module we proposed is employed to fuse the multiple view features into a compact descriptor. This module can not only fully exploit the relevance among multiple views by intra-cluster multi-view fusion mechanism, but also discover the content discrimination by inter-cluster multi-view fusion mechanism. Experimental results on two public datasets, i.e., ModelNet40 and ShapeNetCore55, show that our proposed MVHFN outperforms the current state-of-the-art methods in both the 3D object retrieval and classification tasks.