  Mixed-based point cloud augmentation is a popular solution to the problem of
limited availability of large-scale public datasets. But the mismatch between
mixed points and corresponding semantic labels hinders the further application
in point-wise tasks such as part segmentation. This paper proposes a point
cloud augmentation approach, PointManifoldCut(PMC), which replaces the neural
network embedded points, rather than the Euclidean space coordinates. This
approach takes the advantage that points at the higher levels of the neural
network are already trained to embed its neighbors relations and mixing these
representation will not mingle the relation between itself and its label. We
set up a spatial transform module after PointManifoldCut operation to align the
new instances in the embedded space. The effects of different hidden layers and
methods of replacing points are also discussed in this paper. The experiments
show that our proposed approach can enhance the performance of point cloud
classification as well as segmentation networks, and brings them additional
robustness to attacks and geometric transformations. The code of this paper is
available at: https://github.com/fun0515/PointManifoldCut.
