Semantic labeling is an essential but challenging task when interpreting point clouds of 3D scenes. As a core step for scene interpretation, semantic labeling is the task of annotating every point in the point cloud with a label of semantic meaning, which plays a significant role in plenty of point cloud related applications. For airborne laser scanning (ALS) point clouds, precise annotations can considerably broaden its use in various applications. However, accurate and efficient semantic labeling is still a challenging task, due to the sensor noise, complex object structures, incomplete data, and uneven point densities. In this work, we propose a novel neural network focusing on semantic labeling of ALS point clouds, which investigates the importance of long-range spatial and channel-wise relations and is termed as global relation-aware attentional network (GraNet). GraNet first learns local geometric description and local dependencies using a local spatial discrepancy attention convolution module (LoSDA). In LoSDA, the orientation information, spatial distribution, and elevation information are fully considered by stacking several local spatial geometric learning modules and the local dependencies are learned by using an attention pooling module. Then, a global relation-aware attention module (GRA), consisting of a spatial relation-aware attention module (SRA) and a channel relation-aware attention module (CRA), is presented to further learn attentions from the structural information of a global scope from the relations and enhance high-level features with the long-range dependencies. The aforementioned two important modules are aggregated in the multi-scale network architecture to further consider scale changes in large urban areas. We conducted comprehensive experiments on three ALS point cloud datasets to evaluate the performance of our proposed framework. The results show that our method can achieve higher classification accuracy compared with other commonly used advanced classification methods. For the ISPRS benchmark dataset, our method improves the overall accuracy (OA) to 84.5 % and the average $F_1$ measure (${\mathit{AvgF}}_1$) to 73.6 %, which outperforms other baselines. Besides, experiments were conducted using a new ALS point cloud dataset covering highly dense urban areas and a newly published large-scale dataset.

解释3D场景的点云时，语义标记是一项必不可少但具有挑战性的任务。作为场景解释的核心步骤，语义标记是用语义含义的标签注释点云中的每个点的任务，这在大量与点云相关的应用程序中起着重要的作用。对于机载激光扫描（ALS）点云，精确的注释可以大大扩展其在各种应用中的用途。但是，由于传感器噪声，复杂的对象结构，不完整的数据以及不均匀的点密度，准确而有效的语义标记仍然是一项艰巨的任务。在这项工作中，我们提出了一种新颖的神经网络，重点关注ALS点云的语义标记，它研究了远程空间关系和通道关系的重要性，并被称为全局关系感知注意网络（GraNet）。GraNet首先使用局部空间差异注意力卷积模块（LoSDA）学习局部几何描述和局部依存关系。在LoSDA中，通过堆叠几个局部空间几何学习模块来充分考虑方向信息，空间分布和高程信息，并通过使用注意力集中模块来学习局部依存关系。然后，一个全局关系感知注意模块（GRA），由空间关系感知注意模块（SRA）和通道关系感知注意模块（CRA）组成，提出以进一步从关系的全局范围的结构信息中获取注意力，并增强具有长期依赖关系的高级功能。前面提到的两个重要模块聚集在多尺度网络体系结构中，以进一步考虑大城市地区的尺度变化。我们对三个ALS点云数据集进行了全面的实验，以评估我们提出的框架的性能。结果表明，与其他常用的高级分类方法相比，我们的方法可以实现更高的分类精度。对于ISPRS基准数据集，我们的方法提高了总体准确性（我们对三个ALS点云数据集进行了全面的实验，以评估我们提出的框架的性能。结果表明，与其他常用的高级分类方法相比，我们的方法可以实现更高的分类精度。对于ISPRS基准数据集，我们的方法提高了总体准确性（我们对三个ALS点云数据集进行了全面的实验，以评估我们提出的框架的性能。结果表明，与其他常用的高级分类方法相比，我们的方法可以实现更高的分类精度。对于ISPRS基准数据集，我们的方法提高了总体准确性（OA）达到84.5％的平均值$F_{\mathrm{1个}}$ 措施 （${\mathit{平均值}}_{\mathrm{1个}}$）达到73.6％，超过其他基准。此外，使用覆盖高密度市区的新ALS点云数据集和新发布的大规模数据集进行了实验。