关键词: 关键词: 深度学习, 点云分类, 细粒度分类, 双线性池化, 图神经网络, 注意力机制

Abstract: Abstract: Existing 3D point cloud classification methods struggle to effectively handle fine-grained features and intra-class classification issues. A point cloud fine-grained classification model based on high-frequency attention filtering is proposed. Firstly, a feature dimensionality enhancement module based on spatial cosine encoding is introduced to address the sparsity of point cloud data. This approach preserves more high-frequency fine-grained features without increasing the model's parameter count. Then, a multi-layer bilinear attention feature fusion module is employed to further capture the feature differences in different point cloud neighborhoods. Finally, a graph attention-based high-frequency downsampling module is used to extract feature information of high-frequency points at different scales, with residual connections introduced to fuse features from different levels. The final fused features are used for fine-grained classification. The model achieves an overall classification accuracy of 93.78% on the standard public dataset ModelNet40, and an accuracy of 96.43%, 82.7%, and 79.23% on the three sub-datasets, Airplane, Chair, and Car, of the fine-grained classification dataset FG3D.

Key words: deep learning, point cloud classification, fine-grained classification, bilinear pooling, graph neural networks, attention mechanism