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 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：空载地形激光雷达是一种活跃的遥感技术,它释放近红外光以绘制地球表面物体的地图。激光雷达的衍生产品因其丰富的三维空间信息及其获得多重回报的能力,适合为范围广泛的应用服务。然而,处理点云数据仍然需要在手工编辑方面作出大量努力。某些人为物体由于其形状不同、分布不规则的点云和类别样本数量少而难以探测。在这项工作中,我们提出了一个端至端深层学习框架,以自动检测和分解由被布特环绕的任意数目的激光雷达点所定义的物体。我们的方法是以在物体识别和分解任务上都达到良好性能的点网光版为基础。结果根据人工划定的电源传输塔进行测试,并显示有希望的准确性。