SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network
This work addresses the time-consuming manual labeling issue in 3D scene understanding for applications like robotics and autonomous driving, but it is incremental as it builds on existing semi-supervised segmentation approaches.
The paper tackles the problem of reducing the need for large annotated datasets in 3D point cloud semantic segmentation by proposing SSPC-Net, a semi-supervised network that uses dynamic label propagation and a coupled attention mechanism to generate pseudo labels from few annotated points, achieving better performance than current semi-supervised methods with fewer annotated points.
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.