  Middle-echo, which covers one or a few corresponding points, is a specific
type of 3D point cloud acquired by a multi-echo laser scanner. In this paper,
we propose a novel approach for automatic segmentation of trees that leverages
middle-echo information from LiDAR point clouds. First, using a convolution
classification method, the proposed type of point clouds reflected by the
middle echoes are identified from all point clouds. The middle-echo point
clouds are distinguished from the first and last echoes. Hence, the crown
positions of the trees are quickly detected from the huge number of point
clouds. Second, to accurately extract trees from all point clouds, we propose a
3D deep learning network, PointNLM, to semantically segment tree crowns.
PointNLM captures the long-range relationship between the point clouds via a
non-local branch and extracts high-level features via max-pooling applied to
unordered points. The whole framework is evaluated using the Semantic 3D
reduced-test set. The IoU of tree point cloud segmentation reached 0.864. In
addition, the semantic segmentation network was tested using the Paris-Lille-3D
dataset. The average IoU outperformed several other popular methods. The
experimental results indicate that the proposed algorithm provides an excellent
solution for vegetation segmentation from LiDAR point clouds.
