SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network

Understand

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.