  Real-world sensors often produce incomplete, irregular, and noisy point
clouds, making point cloud completion increasingly important. However, most
existing completion methods rely on large paired datasets for training, which
is labor-intensive. This paper proposes RaPD, a novel semi-supervised point
cloud completion method that reduces the need for paired datasets. RaPD
utilizes a two-stage training scheme, where a deep semantic prior is learned in
stage 1 from unpaired complete and incomplete point clouds, and a
semi-supervised prior distillation process is introduced in stage 2 to train a
completion network using only a small number of paired samples. Additionally, a
self-supervised completion module is introduced to improve performance using
unpaired incomplete point clouds. Experiments on multiple datasets show that
RaPD outperforms previous methods in both homologous and heterologous
scenarios.
