  In view of the difficulty in reconstructing object details in point cloud
completion, we propose a shape prior learning method for object completion. The
shape priors include geometric information in both complete and the partial
point clouds. We design a feature alignment strategy to learn the shape prior
from complete points, and a coarse to fine strategy to incorporate partial
prior in the fine stage. To learn the complete objects prior, we first train a
point cloud auto-encoder to extract the latent embeddings from complete points.
Then we learn a mapping to transfer the point features from partial points to
that of the complete points by optimizing feature alignment losses. The feature
alignment losses consist of a L2 distance and an adversarial loss obtained by
Maximum Mean Discrepancy Generative Adversarial Network (MMD-GAN). The L2
distance optimizes the partial features towards the complete ones in the
feature space, and MMD-GAN decreases the statistical distance of two point
features in a Reproducing Kernel Hilbert Space. We achieve state-of-the-art
performances on the point cloud completion task. Our code is available at
https://github.com/xiaogangw/point-cloud-completion-shape-prior.
