  In this paper, we present a deep learning model that exploits the power of
self-supervision to perform 3D point cloud completion, estimating the missing
part and a context region around it. Local and global information are encoded
in a combined embedding. A denoising pretext task provides the network with the
needed local cues, decoupled from the high-level semantics and naturally shared
over multiple classes. On the other hand, contrastive learning maximizes the
agreement between variants of the same shape with different missing portions,
thus producing a representation which captures the global appearance of the
shape. The combined embedding inherits category-agnostic properties from the
chosen pretext tasks. Differently from existing approaches, this allows to
better generalize the completion properties to new categories unseen at
training time. Moreover, while decoding the obtained joint representation, we
better blend the reconstructed missing part with the partial shape by paying
attention to its known surrounding region and reconstructing this frame as
auxiliary objective. Our extensive experiments and detailed ablation on the
ShapeNet dataset show the effectiveness of each part of the method with new
state of the art results. Our quantitative and qualitative analysis confirms
how our approach is able to work on novel categories without relying neither on
classification and shape symmetry priors, nor on adversarial training
procedures.
