DPLG-Net: Deep Pseudo-Label-Guided Network for Weakly Supervised Semantic Segmentation of ALS Point Clouds

Pseudo-label learning could effectively explore additional signals of unlabeled point clouds. The existing methods mainly utilize category hints of pseudo-label points to assist the training of model. However, they overlook other implicit hints of pseudo-label points, thus leading to limited performance gains. To explore and make full use of these implicit hints, we propose a novel deep pseudo-label-guided network (DPLG-Net) for weakly-supervised semantic segmentation of airborne lidar scanning (ALS) point clouds. Specifically, we first utilize cardinality hints of different categories of pseudo-label points to construct weighted cross-entropy (WCE) loss. This minor design could alleviate the class-imbalance issue of data. Then, we introduce a weighted entropy minimization module which is guided by predictive probability hints of pseudo-label points. It takes the generated pseudo-label points as anchor points, and the predictive probabilities of anchor points are served as weights to calculate weighted entropy of their neighbors, thus providing the direction of entropy minimization. Moreover, we propose a feature learning module for unlabeled points which is guided by semantic feature hints of pseudo-label points. In this module, semantic features of pseudo-label points are employed to produce prototype features of all the categories. By querying the semantic similarity between unlabeled points and category prototype features, the feature learning of unlabeled points is guided towards the most similar category while keeping away from other categories. Experimental results under 0.1% weak labels indicate DPLG-Net achieves state-of-the-art average F1 (71.8%) and overall accuracy (84.1%) on ISPRS dataset; and achieves state-of-the-art average F1 (78.6%) and competitive overall accuracy (87.3%) on LASDU dataset. In addition, we conduct ALS experiment and present a new ALS point cloud dataset named HBUCVC. Experimental results on HBUCVC dataset also validate the effectiveness of DPLG-Net.