Ground extraction from terrestrial LiDAR scans using 2D-3D neighborhood graphs

Abstract

We introduce a new method for filtering terrestrial LiDAR data into two categories: Ground points and object points. Our method consists of four steps. First, we propose a graph-based feature, which is obtained by combining 2D and 3D neighborhood graphs. For each point, we assign a number, that is the count of common neighbors in 2D and 3D graphs. This feature allows the discrimination between terrain points and object points as terrain points tend to have the same neighbors in both 2D and 3D graphs, while off-terrain points tend to have less common neighbors between 2D and 3D graphs. In second step, we used c-mean algorithm to quantize the feature space into two clusters, terrain points and object points. The third step consists of repeating the first and the second step using different neighborhood sizes to construct the KNN(k-nearest neighbor) graph. In the final step, we propose a decisionlevel fusion scheme that combines the results obtained in the third step to achieve higher accuracy. Experiments show the effectiveness of our method.