MEAN: An attention-based approach for 3D mesh shape classification

3D shape processing is a fundamental computer application. Specifically, 3D mesh could provide a natural and detailed way for object representation. However, due to its non-uniform and irregular data structure, applying deep learning technologies to 3D mesh is difficult. Furthermore, previous deep learning approaches for 3D mesh mainly focus on local structural features and there is a loss of information. In this paper, to make better mesh shape awareness, a novel deep learning approach is proposed, which aims to full-use the information of mesh data and exploit comprehensive features for more accurate classification. To utilize self-attention mechanism and learn global features of mesh edges, we propose a novel attention-based structure with the edge attention module. Then, for local feature learning, our model aggregates edge features from adjacent edges. We refine the network by discarding pooling layers for efficiency. Thus, it captures comprehensive features from both local and global fields for better shape awareness. Moreover, we adopt spatial position encoding module based on spatial information of edges to enhance the model to better recognize edges and make full use of mesh data. We demonstrate effectiveness of our model in classification tasks with numerous experiments which show outperforming results on popular datasets.