Hierarchical Graph Attention Based Multi-View Convolutional Neural Network for 3D Object Recognition

Abstract

For multi-view convolutional neural network based 3D object recognition, how to fuse the information of multiple views is a key factor affecting the recognition performance. Most traditional methods use max-pooling algorithm to obtain the final 3D object feature, which does not take into account the correlative information between different views. To make full use of the effective information of multiple views, this paper introduces the hierarchical graph attention based multi-view convolutional neural network for 3D object recognition. At first, the view selection module is proposed to reduce redundant view information in multiple views, which can select the projective views with more effective information. Then, the correlation weighted feature aggregation module is proposed to better fuse multiple view features. Finally, the hierarchical feature aggregation network structure is designed to further to make full use of the correlation information of multiple views. Extensive experimental results have validated the effectiveness of the proposed method.