MHFP: Multi-view based hierarchical fusion pooling method for 3D shape recognition

3D shape recognition has received widespread attention in the field of computer vision. Since the 3D model contains much geometric information, which is difficult to extract but important to feature learning, effective description of 3D shape is still facing great challenges. With the rapid development of deep learning, a large number of methods have been proposed. However, these approaches always focus on the learning of view features but ignore the multi-view information protection in the process of feature fusion. In this work, we propose a novel Multi-view based Hierarchical Fusion Pooling Method (MHFP) for 3D Model Recognition, which hierarchically fuses the features of multi-view into a compact descriptor. Our approach considers the correlation between views, it can powerfully remove redundant information and retain a large amount of essential information. Meanwhile, we design a 3D attention module to dig out the correlation between the views, which prepares for the graph construction. To verify the effectiveness of our MHFP, we conduct experiments on the ModelNet40 dataset and compare it with some state-of-the-art methods. The final results demonstrate the superiority of our proposed approach in 3D shape recognition. (c) 2021 Elsevier B.V. All rights reserved.