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

Authors

  • Mingmei Cheng Nanjing University of Science and Technology
  • Le Hui Nanjing University of Science and Technology
  • Jin Xie Nanjing University of Science and Technology
  • Jian Yang Nanjing University of Science and Technology

Keywords:

3D Computer Vision

Abstract

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 semisupervised segmentation method can achieve better performance than the current semi-supervised segmentation method with fewer annotated 3D points.

Downloads

Published

2021-05-18

How to Cite

Cheng, M., Hui, L., Xie, J., & Yang, J. (2021). SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network. Proceedings of the AAAI Conference on Artificial Intelligence, 35(2), 1140-1147. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16200

Issue

Section

AAAI Technical Track on Computer Vision I