  Recently, arbitrary-scale point cloud upsampling mechanism became
increasingly popular due to its efficiency and convenience for practical
applications. To achieve this, most previous approaches formulate it as a
problem of surface approximation and employ point-based networks to learn
surface representations. However, learning surfaces from sparse point clouds is
more challenging, and thus they often suffer from the low-fidelity geometry
approximation. To address it, we propose an arbitrary-scale Point cloud
Upsampling framework using Voxel-based Network (\textbf{PU-VoxelNet}). Thanks
to the completeness and regularity inherited from the voxel representation,
voxel-based networks are capable of providing predefined grid space to
approximate 3D surface, and an arbitrary number of points can be reconstructed
according to the predicted density distribution within each grid cell. However,
we investigate the inaccurate grid sampling caused by imprecise density
predictions. To address this issue, a density-guided grid resampling method is
developed to generate high-fidelity points while effectively avoiding sampling
outliers. Further, to improve the fine-grained details, we present an auxiliary
training supervision to enforce the latent geometric consistency among local
surface patches. Extensive experiments indicate the proposed approach
outperforms the state-of-the-art approaches not only in terms of fixed
upsampling rates but also for arbitrary-scale upsampling.
