To better capture the local geometric structural information of 3D point clouds,an adaptive graph convolutional 3D point cloud recognition algorithm is proposed based on attention mechanism. To address the drawback of fixed convolutional kernels ignoring features,the algorithm first dynamically learns adaptive convolutional kernels based on graph structural features. Furthermore,to enhance the modeling capability of the model for local geometric structures,the weight distribution of the convolutional kernels using a vector attention mechanism is adjusted adaptively. Subsequently,a graph is constructed using the position features of the point cloud and perform convolution operations on the newly constructed graph structural features using the adaptive convolutional kernels. Finally,new point cloud features through pooling is obtained. Experimental results demonstrate that the proposed algorithm effectively extracts local geometric structural information and achieves higher accuracy in classification tasks even with a limited number of sampled points,outperforming previous point cloud convolutional algorithms. The proposed algorithm also exhibits certain advantages compared to existing methods for point cloud classification and segmentation,as evidenced by the performance evaluation on the ModelNet40,ScanObjectNN,and ShapeNetPart datasets.
3D point cloudsattention mechanismself-adaptationgraph convolutiondynamically learn
维普
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