  Point cloud semantic segmentation is a crucial task in 3D scene
understanding. Existing methods mainly focus on employing a large number of
annotated labels for supervised semantic segmentation. Nonetheless, manually
labeling such large point clouds for the supervised segmentation task is
time-consuming. In order to reduce the number of annotated labels, we propose a
semi-supervised semantic point cloud segmentation network, named SSPC-Net,
where we train the semantic segmentation network by inferring the labels of
unlabeled points from the few annotated 3D points. In our method, we first
partition the whole point cloud into superpoints and build superpoint graphs to
mine the long-range dependencies in point clouds. Based on the constructed
superpoint graph, we then develop a dynamic label propagation method to
generate the pseudo labels for the unsupervised superpoints. Particularly, we
adopt a superpoint dropout strategy to dynamically select the generated pseudo
labels. In order to fully exploit the generated pseudo labels of the
unsupervised superpoints, we furthermore propose a coupled attention mechanism
for superpoint feature embedding. Finally, we employ the cross-entropy loss to
train the semantic segmentation network with the labels of the supervised
superpoints and the pseudo labels of the unsupervised superpoints. Experiments
on various datasets demonstrate that our semi-supervised segmentation method
can achieve better performance than the current semi-supervised segmentation
method with fewer annotated 3D points. Our code is available at
https://github.com/MMCheng/SSPC-Net.
