Airborne Laser Scanning (ALS) point cloud classification is a critical task in remote sensing and photogrammetry communities, which can be widely utilized in urban management, powerline surveying and forest monitoring, etc. In particular, the characteristics of ALS point clouds are distinctive in three aspects, (1) numerous geometric instances (e.g. tracts of roofs); (2) extreme scale variations between different categories (e.g. car v.s. roof); (3) discrepancy distribution along the elevation, which should be specifically focused on for ALS point cloud classification. In this paper, we propose a geometry-attentional network consisting of geometry-aware convolution, dense hierarchical architecture and elevation-attention module to embed the three characteristics effectively, which can be trained in an end-to-end manner. Evaluated on the ISPRS Vaihingen 3D Semantic Labeling benchmark, our method achieves the state-of-the-art performance in terms of average F1 score and overall accuracy (OA). Additionally, without retraining, our model trained on the above Vaihingen 3D dataset can also achieve a better result on the dataset of 2019 IEEE GRSS Data Fusion Contest 3D point cloud classification challenge (DFC 3D) than the baseline (i.e. PointSIFT), which verifies the stronger generalization ability of our model.

机载激光扫描（ALS）点云分类是遥感和摄影测量领域的一项关键任务，可广泛用于城市管理，电力线测量和森林监测等。尤其是，ALS点云的特征在三个方面很明显：（1）大量几何实例（例如，屋顶区域）；（2）不同类别之间的极端比例差异（例如汽车与屋顶）; （3）沿高程的差异分布，对于ALS点云分类应特别关注。在本文中，我们提出了一个由几何感知卷积，密集层次结构和高程注意模块组成的几何注意网络，以有效地嵌入这三个特征，并且可以端到端地对其进行训练。根据ISPRS Vaihingen 3D语义标签基准进行了评估，我们的方法在平均F1得分和总体准确性（OA）方面达到了最先进的性能。此外，无需重新训练，我们在上述Vaihingen 3D数据集上训练的模型在2019 IEEE GRSS数据融合竞赛3D点云分类挑战（DFC 3D）的数据集上也可以获得比基准更好的结果（即 PointSIFT），这验证了我们模型的更强泛化能力。