  Competitive point cloud semantic segmentation results usually rely on a large
amount of labeled data. However, data annotation is a time-consuming and
labor-intensive task, particularly for three-dimensional point cloud data.
Thus, obtaining accurate results with limited ground truth as training data is
considerably important. As a simple and effective method, pseudo labels can use
information from unlabeled data for training neural networks. In this study, we
propose a pseudo-label-assisted point cloud segmentation method with very few
sparsely sampled labels that are normally randomly selected for each class. An
adaptive thresholding strategy was proposed to generate a pseudo-label based on
the prediction probability. Pseudo-label learning is an iterative process, and
pseudo labels were updated solely on ground-truth weak labels as the model
converged to improve the training efficiency. Experiments using the ISPRS 3D
sematic labeling benchmark dataset indicated that our proposed method achieved
an equally competitive result compared to that using a full supervision scheme
with only up to 2$\unicode{x2030}$ of labeled points from the original training
set, with an overall accuracy of 83.7% and an average F1 score of 70.2%.
