Airborne topographic LiDAR is an active remote sensing technology that emits
near-infrared light to map objects on the Earth's surface. Derived products of
LiDAR are suitable to service a wide range of applications because of their
rich three-dimensional spatial information and their capacity to obtain
multiple returns. However, processing point cloud data still requires a
significant effort in manual editing. Certain human-made objects are difficult
to detect because of their variety of shapes, irregularly-distributed point
clouds, and low number of class samples. In this work, we propose an efficient
end-to-end deep learning framework to automatize the detection and segmentation
of objects defined by an arbitrary number of LiDAR points surrounded by
clutter. Our method is based on a light version of PointNet that achieves good
performance on both object recognition and segmentation tasks. The results are
tested against manually delineated power transmission towers and show promising
accuracy.

本文提出了一种基于深度学习的 LiDAR 点云数据目标检测和分割方法，采用了 PointNet 网络模型，成功实现了对各种形状复杂、点云分布不均匀的人工目标（如电力塔）的高效检测和分割。