  A major endeavor of computer vision is to represent, understand and extract
structure from 3D data. Towards this goal, unsupervised learning is a powerful
and necessary tool. Most current unsupervised methods for 3D shape analysis use
datasets that are aligned, require objects to be reconstructed and suffer from
deteriorated performance on downstream tasks. To solve these issues, we propose
to extend the InfoMax and contrastive learning principles on 3D shapes. We show
that we can maximize the mutual information between 3D objects and their
"chunks" to improve the representations in aligned datasets. Furthermore, we
can achieve rotation invariance in SO${(3)}$ group by maximizing the mutual
information between the 3D objects and their geometric transformed versions.
Finally, we conduct several experiments such as clustering, transfer learning,
shape retrieval, and achieve state of art results.
