  Unsupervised point cloud shape correspondence aims to obtain dense
point-to-point correspondences between point clouds without manually annotated
pairs. However, humans and some animals have bilateral symmetry and various
orientations, which lead to severe mispredictions of symmetrical parts.
Besides, point cloud noise disrupts consistent representations for point cloud
and thus degrades the shape correspondence accuracy. To address the above
issues, we propose a Self-Ensembling ORientation-aware Network termed SE-ORNet.
The key of our approach is to exploit an orientation estimation module with a
domain adaptive discriminator to align the orientations of point cloud pairs,
which significantly alleviates the mispredictions of symmetrical parts.
Additionally, we design a selfensembling framework for unsupervised point cloud
shape correspondence. In this framework, the disturbances of point cloud noise
are overcome by perturbing the inputs of the student and teacher networks with
different data augmentations and constraining the consistency of predictions.
Extensive experiments on both human and animal datasets show that our SE-ORNet
can surpass state-of-the-art unsupervised point cloud shape correspondence
methods.
