  Point cloud completion aims to recover raw point clouds captured by scanners
from partial observations caused by occlusion and limited view angles. This
makes it hard to recover details because the global feature is unlikely to
capture the full details of all missing parts. In this paper, we propose a
novel approach to point cloud completion task called Point-PC, which uses a
memory network to retrieve shape priors and designs a causal inference model to
filter missing shape information as supplemental geometric information to aid
point cloud completion. Specifically, we propose a memory operating mechanism
where the complete shape features and the corresponding shapes are stored in
the form of ``key-value'' pairs. To retrieve similar shapes from the partial
input, we also apply a contrastive learning-based pre-training scheme to
transfer the features of incomplete shapes into the domain of complete shape
features. Experimental results on the ShapeNet-55, PCN, and KITTI datasets
demonstrate that Point-PC outperforms the state-of-the-art methods.
