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Linked Attention-Based Dynamic Graph Convolution Module for Point Cloud Classification

With the rapid development of 3D technology, point cloud data is becoming more and more popular, which arouses researchers’ interest. But its properties – irregularity and disorder – make it difficult to analyze. In this work, we combine the attention module with the dynamic graph convolutional neural network to pay attention to the target’s critical part. Then, the modules are densely connected to guarantee that each layer is fully utilized. Finally, we carry out experiments on several benchmark datasets to verify the proposed model and achieve state-of-the-art performance.