  Motivated by the intuition that one can transform two aligned point clouds to
each other more easily and meaningfully than a misaligned pair, we propose
CorrNet3D -- the first unsupervised and end-to-end deep learning-based
framework -- to drive the learning of dense correspondence between 3D shapes by
means of deformation-like reconstruction to overcome the need for annotated
data. Specifically, CorrNet3D consists of a deep feature embedding module and
two novel modules called correspondence indicator and symmetric deformer.
Feeding a pair of raw point clouds, our model first learns the pointwise
features and passes them into the indicator to generate a learnable
correspondence matrix used to permute the input pair. The symmetric deformer,
with an additional regularized loss, transforms the two permuted point clouds
to each other to drive the unsupervised learning of the correspondence. The
extensive experiments on both synthetic and real-world datasets of rigid and
non-rigid 3D shapes show our CorrNet3D outperforms state-of-the-art methods to
a large extent, including those taking meshes as input. CorrNet3D is a flexible
framework in that it can be easily adapted to supervised learning if annotated
data are available. The source code and pre-trained model will be available at
https://github.com/ZENGYIMING-EAMON/CorrNet3D.git.
