Airborne LiDAR point clouds classification has been a challenging task due to the characteristics of point clouds and the complexity of the urban environment. Recently, methods that directly act on unordered point set have achieved satisfactory results in point clouds classification. However, the existing methods that directly consume point clouds pay little attention to the interaction between the deep layers, which makes the feature learning insufficient in complex environments. In this paper, we propose a deep neural network for semantic labeling task. It iteratively learns deep features in a hierarchical structure, and provides a simple but efficient way to make interactions between different hierarchical levels. Since iteration process will greatly increase the number of layers, we employ the residual network to improve the performance. In addition, we also introduce dilated k-nearest neighbors and multi-scale grouping to increase the receptive field. The experiments on both Vaihingen 3D dataset and Dayton Annotated LiDAR Earth Scan (DALES) dataset demonstrate the effectiveness of the proposed method in point cloud classification.

由于点云的特性和城市环境的复杂性，机载 LiDAR 点云分类一直是一项具有挑战性的任务。最近，直接作用于无序点集的方法在点云分类中取得了令人满意的结果。然而，现有的直接消费点云的方法很少关注深层之间的交互，这使得复杂环境下的特征学习不足。在本文中，我们提出了一种用于语义标记任务的深度神经网络。它迭代地学习分层结构中的深层特征，并提供一种简单但有效的方法来进行不同层次级别之间的交互。由于迭代过程会大大增加层数，我们采用残差网络来提高性能。此外，我们还引入了扩张的 k-最近邻和多尺度分组来增加感受野。在 Vaihingen 3D 数据集和 Dayton Annotated LiDAR Earth Scan (DALES) 数据集上的实验证明了所提出的方法在点云分类中的有效性。