  3D point cloud semantic segmentation is a challenging topic in the computer
vision field. Most of the existing methods in literature require a large amount
of fully labeled training data, but it is extremely time-consuming to obtain
these training data by manually labeling massive point clouds. Addressing this
problem, we propose a superpoint-guided semi-supervised segmentation network
for 3D point clouds, which jointly utilizes a small portion of labeled scene
point clouds and a large number of unlabeled point clouds for network training.
The proposed network is iteratively updated with its predicted pseudo labels,
where a superpoint generation module is introduced for extracting superpoints
from 3D point clouds, and a pseudo-label optimization module is explored for
automatically assigning pseudo labels to the unlabeled points under the
constraint of the extracted superpoints. Additionally, there are some 3D points
without pseudo-label supervision. We propose an edge prediction module to
constrain features of edge points. A superpoint feature aggregation module and
a superpoint feature consistency loss function are introduced to smooth
superpoint features. Extensive experimental results on two 3D public datasets
demonstrate that our method can achieve better performance than several
state-of-the-art point cloud segmentation networks and several popular
semi-supervised segmentation methods with few labeled scenes.
