论文标题
混乱的空气传球点云的物体分割
Object Segmentation of Cluttered Airborne LiDAR Point Clouds
论文作者
论文摘要
机载地形LIDAR是一种活跃的遥感技术，它发出近红外的光线以绘制地球表面上的物体。 LIDAR的衍生产品适合为广泛的应用服务，因为它们具有丰富的三维空间信息及其获得多个回报的能力。但是，处理点云数据仍然需要在手动编辑中付出重大努力。由于某些人为物体的形状多样，不规则分布点云和类样本数量少，因此难以检测到某些人为物体。在这项工作中，我们提出了一个有效的端到端深度学习框架，以自动化被任意数量的激光点所定义的对象的检测和分割。我们的方法基于PointNet的轻型版本，该版本在对象识别和细分任务上都能达到良好的性能。结果对手动划定的电力传输塔进行了测试，并显示出有希望的准确性。
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.
