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In view of the difficulty in reconstructing object details in point cloudcompletion, we propose a shape prior learning method for object completion. Theshape priors include geometric information in both complete and the partialpoint clouds. We design a feature alignment strategy to learn the shape priorfrom complete points, and a coarse to fine strategy to incorporate partialprior in the fine stage. To learn the complete objects prior, we first train apoint cloud auto-encoder to extract the latent embeddings from complete points.Then we learn a mapping to transfer the point features from partial points tothat of the complete points by optimizing feature alignment losses. The featurealignment losses consist of a L2 distance and an adversarial loss obtained byMaximum Mean Discrepancy Generative Adversarial Network (MMD-GAN). The L2distance optimizes the partial features towards the complete ones in thefeature space, and MMD-GAN decreases the statistical distance of two pointfeatures in a Reproducing Kernel Hilbert Space. We achieve state-of-the-artperformances on the point cloud completion task. Our code is available athttps://github.com/xiaogangw/point-cloud-completion-shape-prior.

Point Cloud Completion by Learning Shape Priors