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Competitive point cloud semantic segmentation results usually rely on a largeamount of labeled data. However, data annotation is a time-consuming andlabor-intensive task, particularly for three-dimensional point cloud data.Thus, obtaining accurate results with limited ground truth as training data isconsiderably important. As a simple and effective method, pseudo labels can useinformation from unlabeled data for training neural networks. In this study, wepropose a pseudo-label-assisted point cloud segmentation method with very fewsparsely sampled labels that are normally randomly selected for each class. Anadaptive thresholding strategy was proposed to generate a pseudo-label based onthe prediction probability. Pseudo-label learning is an iterative process, andpseudo labels were updated solely on ground-truth weak labels as the modelconverged to improve the training efficiency. Experiments using the ISPRS 3Dsematic labeling benchmark dataset indicated that our proposed method achievedan equally competitive result compared to that using a full supervision schemewith only up to 2$\unicode{x2030}$ of labeled points from the original trainingset, with an overall accuracy of 83.7% and an average F1 score of 70.2%.

Weakly Supervised Pseudo-Label assisted Learning for ALS Point Cloud Semantic Segmentation