Object Segmentation of Cluttered Airborne LiDAR Point Clouds
This addresses the challenge of automating object segmentation in cluttered airborne LiDAR data for remote sensing applications, but it is incremental as it adapts an existing method to a specific domain.
The paper tackles the problem of manually editing airborne LiDAR point clouds for object detection and segmentation by proposing an efficient end-to-end deep learning framework based on a light version of PointNet, achieving promising accuracy on power transmission towers.
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.