  While there are novel point cloud semantic segmentation schemes that
continuously surpass state-of-the-art results, the success of learning an
effective model usually rely on the availability of abundant labeled data.
However, data annotation is a time-consuming and labor-intensive task,
particularly for large-scale airborne laser scanning (ALS) point clouds
involving multiple classes in urban areas. Thus, how to attain promising
results while largely reducing labeling works become an essential issue. In
this study, we propose a deep-learning based weakly supervised framework for
semantic segmentation of ALS point clouds, exploiting potential information
from unlabeled data subject to incomplete and sparse labels. Entropy
regularization is introduced to penalize the class overlap in predictive
probability. Additionally, a consistency constraint by minimizing the
discrepancy distance between instant and ensemble predictions is designed to
improve the robustness of predictions. Finally, we propose an online soft
pseudo-labeling strategy to create extra supervisory sources in an efficient
and nonpaprametric way. Extensive experimental analysis using three benchmark
datasets demonstrates that in case of sparse point annotations, our proposed
method significantly boosts the classification performance without compromising
the computational efficiency. It outperforms current weakly supervised methods
and achieves a comparable result against full supervision competitors. For the
ISPRS 3D Labeling Vaihingen data, by using only 0.1% of labels, our method
achieves an overall accuracy of 83.0% and an average F1 score of 70.0%, which
have increased by 6.9% and 12.8% respectively, compared to model trained by
sparse label information only.
