PCN is a learning-based shape completion method that maps partial point clouds to complete ones without voxelization. The network is trained on ShapeNet data and directly outputs dense point clouds. It uses a decoder architecture with fully connected layers followed by a folding module to generate the completed shape from a latent feature vector extracted from the input.