Multi-view Feature Learning for 3D Model Classification

In today's digital era, 3D models have become a key hot data form, which raises the critical issue of how to accurately and efficiently extract key features from massive 3D models. In existing view-based classification methods, views from different angles are often treated equally, ignoring the differences and importance of views at different angles. To solve these problems, this paper proposes a 3D model classification method based on multi-angle view fusion. First, multi-angle representative views of the model are obtained by setting viewpoints at 30° north latitude around the 3D model, with an interval of 60° between adjacent viewpoints. Then, the attention mechanism is introduced into the network to effectively extract deep features of the views. In the test phase, different weights are assigned to view features from different angles, and finally, a voting mechanism is used to complete the classification of 3D models. Experimental results show that under the optimal configuration (PreAct-ResNet+CBAM3+soft voting with hyperparameter tuning), the classification accuracy of this method on the ModelNet10 dataset reaches 96.61%, demonstrating outstanding classification performance.