Multi-view 3D model recognition via multi-label and multi-level fusion with bidirectional GRU

Abstract

This paper proposes a multi-view fusion-based recognition algorithm to address the imbalanced recognition accuracy of existing 3D model multi-view recognition methods. This imbalance arises from their failure to account for inter-view feature consistency and their use of inefficient fusion strategies. The proposed algorithm employs a dual-branch feature extraction network and a multi-label loss function to enforce the learning of consistent features across different views of the same model. Concurrently, a multi-level Gated Recurrent Unit (GRU) fusion network is constructed to efficiently integrate high-dimensional features from various levels and temporal information across multiple views. Simulation results demonstrate that the proposed algorithm achieves highly competitive recognition accuracy on mainstream benchmark datasets. Furthermore, it exhibits more balanced performance across different categories, thereby showing enhanced stability and robustness.