Semantic Segmentation of Airborne LiDAR Point Clouds With Noisy Labels

Abstract

High-quality point cloud annotation is labor-intensive and time-consuming, but it serves as a critical factor driving the success of LiDAR point cloud semantic segmentation. Leveraging low-quality labels in LiDAR point cloud processing is overlooked, despite the fact that noisy annotation has low labeling costs and abundant cross-modal resources (e.g., labels from images). To this end, we thoroughly investigate the performance of airborne LiDAR point cloud semantic segmentation models using noisy labels for the first time and find that it is closely related to object categories and learning stages. Then we propose a new semantic segmentation framework for LiDAR point cloud noisy learning called adaptive dynamic noise label correction (ADNLC), which consists of weak category priority, dynamic monitoring (DM), and historical choice (HC). With these methods, we can adaptively correct the noise labels of different categories according to their specific learning situations. Finally, we provide a comprehensive process for noise simulation, accuracy evaluation, and comparisons in airborne LiDAR point cloud learning from noisy labels. We conduct experiments on the ISPRS 3-D Labeling Vaihingen and Large-scale ALS data for Semantic Labeling in Dense Urban Areas (LASDU) datasets, and the results show that our ADNLC outperforms baseline methods by 30% and 16%, respectively, verifying the superiority of ADNLC and demonstrating the potential of noise labels in LiDAR data processing.