  We propose a canonical point autoencoder (CPAE) that predicts dense
correspondences between 3D shapes of the same category. The autoencoder
performs two key functions: (a) encoding an arbitrarily ordered point cloud to
a canonical primitive, e.g., a sphere, and (b) decoding the primitive back to
the original input instance shape. As being placed in the bottleneck, this
primitive plays a key role to map all the unordered point clouds on the
canonical surface and to be reconstructed in an ordered fashion. Once trained,
points from different shape instances that are mapped to the same locations on
the primitive surface are determined to be a pair of correspondence. Our method
does not require any form of annotation or self-supervised part segmentation
network and can handle unaligned input point clouds. Experimental results on 3D
semantic keypoint transfer and part segmentation transfer show that our model
performs favorably against state-of-the-art correspondence learning methods.
