Point Cloud Classification Network Based on Dynamic Graph Convolution

With the continuous development of 3D data acquisition technology, point cloud data, an essential form of 3D data representation is widely used in fields such as autonomous driving, indoor navigation, virtual reality, and augmented reality. As an essential branch of point cloud data processing, cloud classification has important research significance and value. However, this task poses challenges due to the sparsity, irregularity, and unordered nature of point cloud data. Among most of the methods dealing with this problem, there are problems of inadequate extraction of local features, low accuracy of classification, and poor generalization of point cloud data. Therefore, this paper addresses the above issue. This paper presents an improved 3D point cloud classification network by enhancing the Dynamic Graph Convolutional Neural Network (DGCNN). Firstly, this paper combines K-Nearest Neighbors (KNN) and ball radius query for feature extraction to better capture the local structural information in cloud classification data. Secondly, a conventional convolution layer is incorporated between the second and third graph convolution layers to enhance the feature representation in the proposed approach. Finally, this paper uses a global pooling method that combines maximum pooling and average pooling to construct the global structure of the point cloud while preserving the most critical information. Realize 3D point cloud classification. The experimental results demonstrate a 0.9% improvement in accuracy on the ModelNet40 dataset using the proposed improved method. This validates the effectiveness of the enhancement process described in this paper and offers valuable insights for enhancing the performance and application of point cloud classification.