Improved RPMNet for 3D feature extraction based on DGCNN

Feature extraction is a critical step in point cloud registration networks, determining how effectively point cloud data is represented. The feature extraction module in RPMNet uses fixed-range local features as input, whereas DGCNN dynamically constructs adjacency graphs and uses graph convolutions for feature updates, better capturing both local and global features of the point cloud. This study aims to explore the application of the Dynamic Graph Convolutional Neural Network (DGCNN) in RPMNet’s feature module to address the inadequacies in local and global feature extraction and improve its registration accuracy in point cloud data processing, particularly on the ModelNet40 dataset. The study conducted training and testing on the ModelNet40 dataset, comprising 5124 training point clouds, 1198 validation point clouds, and 1245 test point clouds. Through performance comparison analysis, various evaluation metrics such as rotation error and translation error were used to assess the model’s performance. The results show that the DGCNN-enhanced RPMNet reduced the isotropic rotation error from 0.056 to 0.053. This indicates that applying DGCNN to RPMNet can dynamically capture local and global features of point clouds, improving feature representation accuracy and model robustness. These findings are significant for the field of point cloud data processing, validating the effectiveness of DGCNN and providing new directions for future research. This advancement promotes the application of graph neural networks in practical problems, enhancing the technical level of the related field.