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.

翻译：机载地形激光雷达是一种主动遥感技术，通过发射近红外光来测绘地球表面地物。由于其富含三维空间信息且能获取多次回波，激光雷达的衍生产品可服务于广泛的应用领域。然而，点云数据的处理仍需大量人工编辑工作。某些人造目标因形状多样、点云分布不规则及类别样本稀少而难以检测。本研究提出一种高效的端到端深度学习框架，用于自动检测和分割被杂乱点云包围、由任意数量激光雷达点定义的目标。该方法基于轻量级PointNet架构，在目标识别与分割任务中均取得良好性能。实验结果与人工标注的输电塔数据进行对比验证，显示出较高的准确度。