Individual tree extraction from urban mobile laser scanning (MLS) point clouds is important for many urban applications. Recently, deep learning-based semantic segmentation of urban MLS point clouds has achieved significant progress, which makes it possible to segment tree point clouds. However, tree segments often are spatially overlapping with varying shapes and incompleteness caused by occlusion, which makes individual tree extraction a challenging task. In this paper, we propose a novel top-down approach to extract individual trees from urban MLS point clouds. Firstly, a semantic segmentation deep network is applied to segment tree points from raw urban MLS point clouds, and then the segmented tree points are further grouped into a set of tree clusters using Euclidean distance clustering. Next, a pointwise direction embedding deep network (PDE-net) is proposed to predict the direction vectors pointing to tree centers for each tree cluster to enhance the boundaries of instance-level trees. After that, a direction aggregation-based strategy is developed to detect the tree centers for each tree cluster, and the clusters are classified into single-tree clusters and multi-tree clusters based on the number of detected tree centers. Finally, the single-tree clusters are directly extracted as individual trees, while the multi-tree clusters are further separated into instance-level trees based on our proposed accessible region growing algorithm combining the embedded pointwise directions and detected tree centers. Four MLS point clouds collected from different urban scenes were used to evaluate the performance of the proposed method. The precision, recall, and F-score of 0.96, 0.94, and 0.95, respectively, on these four datasets demonstrate the effectiveness of our approach. An implementation of the proposed method is available at: https://github.com/HiphonL/IndividualTreeExtraction.

从城市移动激光扫描（MLS）点云中提取单个树对于许多城市应用而言非常重要。最近，基于深度学习的城市MLS点云语义分割取得了重大进展，这使得对树形点云进行分割成为可能。但是，树段通常在空间上重叠，形状不同，并且由于遮挡而导致不完整，这使得单个树的提取成为一项艰巨的任务。在本文中，我们提出了一种新颖的自顶向下方法，用于从城市MLS点云中提取单个树木。首先，使用语义分割深度网络对原始城市MLS点云中的树点进行分割，然后使用欧氏距离聚类将分割后的树点进一步分组为一组树簇。下一个，提出了一种逐点方向嵌入深度网络（PDE-net），以预测指向每个树簇的树中心的方向矢量，以增强实例级树的边界。此后，开发了一种基于方向聚合的策略来检测每个树簇的树中心，并且根据检测到的树中心的数量将簇分为单树簇和多树簇。最后，根据我们提出的结合嵌入式点方向和检测到的树中心的可访问区域增长算法，将单树群集直接提取为单​​独的树，而将多树群集进一步分离为实例级树。利用从不同城市场景收集的四个MLS点云来评估该方法的性能。在这四个数据集上的精度，召回率和F分数分别为0.96、0.94和0.95，证明了我们方法的有效性。提议的方法的实现可从以下网址获得：https：//github.com/HiphonL/IndividualTreeExtraction。