Efficient 3D Model Classification Using DecoupleNet with Wavelet-ECA Feature Integration

As deep learning techniques continue to advance in 3D model classification, traditional models often face challenges in resource-constrained environments, where balancing computational efficiency and classification accuracy becomes crucial. In this paper, we propose a lightweight 3D model classification framework based on the DecoupleNet network to enhance classification performance in such environments. To address the issue of lower accuracy with fewer parameters, we introduce a novel dual-branch Feature Integration Downsampling (WED) module, which combines wavelet transform downsampling, wavelet pooling, and the ECA attention mechanism. This approach preserves low-frequency structural information while enhancing high-frequency details, all while maintaining a lightweight structure. The WED module effectively balances global structural and local detailed features, improving feature discriminability and selectivity. Experimental results on the ModelNet10 dataset show a 1.72 % accuracy improvement over the original DecoupleNet, demonstrating the proposed method's efficiency and robustness in 3D model classification tasks.