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This paper presents an automated algorithm for extracting 3D trees directly from 3D mobile light detection and ranging (LiDAR)\uddata. To reduce both computational and spatial complexities, ground points are first filtered out from a raw 3D point cloud via blockbased\udelevation filtering. Off-ground points are then grouped into clusters representing individual objects through Euclidean distance\udclustering and voxel-based normalized cut segmentation. Finally, a model-driven method is proposed to achieve the extraction of 3D\udtrees based on a pairwise 3D shape descriptor. The proposed algorithm is tested using a set of mobile LiDAR point clouds acquired\udby a RIEGL VMX-450 system. The results demonstrate the feasibility and effectiveness of the proposed algorithm

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Automated Extraction of 3D Trees from Mobile LiDAR Point Clouds