PCRNet+: A Point Cloud Alignment Algorithm Introducing Dynamic Graph Convolutional Neural Networks

In this paper, an improved point cloud alignment network based on PCRNet is proposed. In the improved model, DGCNN is used as a feature extractor to capture the local and global geometric features of the point cloud, and a fully connected layer is used for feature fusion and rigid-body transformation parameter prediction. Compared with the original PCRNet, the improved network shows higher accuracy and robustness in the point cloud alignment task. In order to verify the performance of the improved network, two classical algorithms, ICP and FGR, are used as benchmarks in our experiment, and experimental comparisons of PCRNet and its improved version are performed under noise-free and noise-containing conditions, respectively. The experimental results show that the improved network (PCRNet+) proposed in our approach outperforms the original PCRNet under different test conditions, including experiments conducted in noise-free, noise-containing, and occlusion scenarios. Specifically, under noise-containing conditions, PCRNet+ surpasses the next-best algorithm, FGR, by over 95.9% across three key metrics. In occlusion scenarios, PCRNet+ achieves more than 100% improvement in all evaluated metrics compared to FGR.