An optimal hierarchical clustering approach to segmentation of mobile LiDAR point clouds
This work addresses segmentation challenges in mobile LiDAR data processing, which is important for applications like autonomous driving and mapping, but it appears incremental as it builds on existing hierarchical clustering with optimization techniques.
The paper tackles the problem of segmenting mobile LiDAR point clouds by proposing an optimal hierarchical clustering approach that optimizes cluster combinations using bipartite graph matching and minimum-cost perfect matching, resulting in automated segmentation of multiple individual objects that outperforms state-of-the-art methods.
This paper proposes a hierarchical clustering approach for the segmentation of mobile LiDAR point clouds. We perform the hierarchical clustering on unorganized point clouds based on a proximity matrix. The dissimilarity measure in the proximity matrix is calculated by the Euclidean distances between clusters and the difference of normal vectors at given points. The main contribution of this paper is that we succeed to optimize the combination of clusters in the hierarchical clustering. The combination is obtained by achieving the matching of a bipartite graph, and optimized by solving the minimum-cost perfect matching. Results show that the proposed optimal hierarchical clustering (OHC) succeeds to achieve the segmentation of multiple individual objects automatically and outperforms the state-of-the-art LiDAR point cloud segmentation approaches.