Local information fusion network for 3D shape classification and retrieval

Abstract

Due to the wide applications of 3D shapes, exploring effective 3D shape recognition algorithms has attracted much research attention. Various approaches have been proposed in recent years, within which the multi-view based methods show their promising performances. Previous multi-view based methods mainly extract global features of views by employing the well-established CNN and then explore the correlations between the view-level descriptors for 3D shape representation. However, these approaches ignore the local characteristics extraction of view images. Besides, they also lack the consideration for the relationships between image regions and feature map channels, which could provide more detailed and descriptive information to improve the discrimination of shape descriptors. To address these issues, we propose a novel Local Information Fusion Network (LIFN) for local characteristics extraction and relationships exploration based on the feature maps during the convolution process. Concretely, the Region Organization Module (ROM) is introduced for feature map reorganization and region-wise feature extraction. Besides, the Region-wise Attention (RWA) and Channel-wise Attention (CWA) are designed for region-wise and channel-wise interaction exploration, respectively. Extensive experiments on the ModelNet40 database demonstrate the superiority of our proposed network against the state-of-the-art approaches.