  Point cloud completion estimates complete shapes from incomplete point clouds
to obtain higher-quality point cloud data. Most existing methods only consider
global object features, ignoring spatial and semantic information of adjacent
points. They cannot distinguish structural information well between different
object parts, and the robustness of models is poor. To tackle these challenges,
we propose an information interaction-based generative network for point cloud
completion ($\mathbf{DualGenerator}$). It contains an adversarial generation
path and a variational generation path, which interact with each other and
share weights. DualGenerator introduces a local refinement module in generation
paths, which captures general structures from partial inputs, and then refines
shape details of the point cloud. It promotes completion in the unknown region
and makes a distinction between different parts more obvious. Moreover, we
design DGStyleGAN to improve the generation quality further. It promotes the
robustness of this network combined with fusion analysis of dual-path
completion results. Qualitative and quantitative evaluations demonstrate that
our method is superior on MVP and Completion3D datasets. The performance will
not degrade significantly after adding noise interference or sparse sampling.
