  Point cloud completion aims to recover the complete shape based on a partial
observation. Existing methods require either complete point clouds or multiple
partial observations of the same object for learning. In contrast to previous
approaches, we present Partial2Complete (P2C), the first self-supervised
framework that completes point cloud objects using training samples consisting
of only a single incomplete point cloud per object. Specifically, our framework
groups incomplete point clouds into local patches as input and predicts masked
patches by learning prior information from different partial objects. We also
propose Region-Aware Chamfer Distance to regularize shape mismatch without
limiting completion capability, and devise the Normal Consistency Constraint to
incorporate a local planarity assumption, encouraging the recovered shape
surface to be continuous and complete. In this way, P2C no longer needs
multiple observations or complete point clouds as ground truth. Instead,
structural cues are learned from a category-specific dataset to complete
partial point clouds of objects. We demonstrate the effectiveness of our
approach on both synthetic ShapeNet data and real-world ScanNet data, showing
that P2C produces comparable results to methods trained with complete shapes,
and outperforms methods learned with multiple partial observations. Code is
available at https://github.com/CuiRuikai/Partial2Complete.
