Internal-External View Based for Universal 3D Model Retrieval and Classification

The view-based 3D model retrieval and classification algorithms lack the internal structural features of 3D models and require many views to achieve universal recognition of both non-rigid and rigid 3D models. A 3D model retrieval and classification algorithm based on internal and external views is proposed to address this issue. First, an internal view extraction module is proposed to capture internal structural features; then, an external view extraction module is introduced based on tetrahedral projection to obtain external shape features. Finally, a universal feature extraction network is used for feature extraction and fusion learning of internal and external views, applying this to 3D model retrieval and classification tasks. In the rigid 3D model dataset ModelNet40 and the non-rigid dataset SHREC15, the mean average precision reached 93.3% and 98.9%, respectively, with overall accuracies of 94.8% and 99.4%. Additionally, in the ShapeNet Core55, the micro-average and macro-average accuracy and recall metrics demonstrated excellent performance. This indicates that the proposed algorithm captures significant discriminative and universal features with limited views.