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.

本文提出了一种基于深度学习的弱监督 ALS 点云语义分割框架，在不完整和稀疏标记的未标记数据中利用潜在信息。通过熵正则化、一致性约束和在线软伪标记策略等，显著提高了分类性能，且效率高于当前弱监督方法。ISPRS 3D Labeling Vaihingen 数据集上，该方法只使用 0.1％ 的标记，达到了 83.0％ 的总体精度和 70.0％ 的平均 F1 分数，相较于只使用稀疏标记信息的模型，其提高了 6.9％ 和 12.8％。