3D Model Classification Method Based on EfficientNetV2 with Wavelet Transform Convolution and Multi-View Feature Fusion

This study proposes a 3D model classification method that combines wavelet transform convolution and multi-view feature fusion, addressing the issue that traditional convolutional neural networks overlook high-frequency details and contextual information in images. The method first selects nine representative views of the 3D model and incorporates wavelet transform convolution into the EfficientNetV2 backbone network for global feature learning, enhancing the ability to capture features from different frequency domains. Additionally, a context-anchor attention mechanism is introduced to further improve detail recovery. Experimental results demonstrate that the proposed method achieves classification accuracies of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 1. 1 5 \%}$</tex> and 87.72 % on the ModelNet10 and ModelNet40 datasets, respectively, showing significant improvements over traditional convolutional neural networks and baseline models, particularly excelling in high-detail tasks.