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PU-WGCN: Point Cloud Upsampling Using Weighted Graph Convolutional Networks

Point clouds are sparse and unevenly distributed, which makes upsampling a challenging task. The current upsampling algorithm encounters the problem that neighboring nodes are similar in terms of specific features, which tends to produce hole overfilling and boundary blurring. The local feature variability of the point cloud is small, and the aggregated neighborhood feature operation treats all neighboring nodes equally. These two reasons make the local node features too similar. We designed the graph feature enhancement module to reduce the similarity between different nodes as a solution to the problem. In addition, we calculate the feature similarity between neighboring nodes based on both spatial information and features of the point cloud, which is used as the boundary weight of the point cloud graph to solve the problem of boundary blurring. We fuse the graph feature enhancement module with the boundary information weighting module to form the weighted graph convolutional networks (WGCN). Finally, we combine the WGCN module with the upsampling module to form a point cloud upsampling network named PU-WGCN. Compared with other upsampling networks, the experimental results show that PU-WGCN can solve the problems of hole overfilling and boundary blurring and improve the upsampling accuracy.