PU-GAT: Point cloud upsampling with graph attention network

Point cloud upsampling has been extensively studied, however, the existing approaches suffer from the losing of structural information due to neglect of spatial dependencies between points. In this work, we propose PU-GAT, a novel 3D point cloud upsampling method that leverages graph attention networks to learn structural information over the baselines. Specifically, we first design a local–global feature extraction unit by combining spatial information and position encoding to mine the local spatial inter-dependencies across point features. Then, we construct an up-down-up feature expansion unit, which uses graph attention and GCN to enhance the ability of capturing local structure information. Extensive experiments on synthetic and real data have shown that our method achieves superior performance against previous methods quantitatively and qualitatively.