FDCPNet:feature discrimination and context propagation network for 3D shape representation

Background: Three-dimensional (3D) shape representation using mesh data is essential in various applications, such as virtual reality and simulation technologies. Current methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas, which affects the overall precision. To address these issues, we propose the Feature Discrimination and Context Propagation Network (FDCPNet), which is a novel approach that synergistically integrates local and global features in mesh datasets. Methods: FDCPNet is composed of two modules: (1) the Feature Discrimination Module, which employs an attention mechanism to enhance the identification of key local features, and (2) the Context Propagation Module, which enriches key local features by integrating global contextual information, thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh model. Results: Experiments on popular datasets validated the effectiveness of FDCPNet, showing an improvement in the classification accuracy over the baseline MeshNet. Furthermore, even with reduced mesh face numbers and limited training data, FDCPNet achieved promising results, demonstrating its robustness in scenarios of variable complexity.