Object Segmentation of Cluttered Airborne LiDAR Point Clouds

TL;DR

This paper introduces an efficient deep learning framework based on a lightweight PointNet variant for automatic object detection and segmentation in cluttered airborne LiDAR point clouds, improving automation in processing complex 3D spatial data.

Contribution

The work presents a novel end-to-end deep learning approach tailored for segmenting arbitrary objects in LiDAR data, addressing challenges of irregular shapes and sparse samples.

Findings

Achieved promising accuracy on power transmission tower segmentation

Demonstrated effectiveness of a lightweight PointNet model

Reduced manual editing effort in LiDAR data processing

Abstract

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.