An optimal hierarchical clustering approach to segmentation of mobile LiDAR point clouds
Sheng Xu, Ruisheng Wang, Han Zheng
TL;DR
This paper introduces an optimal hierarchical clustering method for segmenting mobile LiDAR point clouds, improving accuracy by optimizing cluster combinations through bipartite graph matching.
Contribution
The main novelty is the optimization of cluster merging in hierarchical clustering using bipartite graph matching for LiDAR point cloud segmentation.
Findings
Successfully segments multiple objects automatically
Outperforms existing LiDAR segmentation methods
Achieves more accurate and reliable segmentation results
Abstract
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.
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