Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5, 629-632, 2014
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-5/629/2014/
doi:10.5194/isprsarchives-XL-5-629-2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
06 Jun 2014
Automated Extraction of 3D Trees from Mobile LiDAR Point Clouds
Y. Yu1, J. Li2,3, H. Guan3, D. Zai1, and C. Wang1 1Department of Computer Science, Xiamen University, 422 Siming Road South, Xiamen, FJ 361005, China
2Key Laboratory of Underwater Acoustic Communication and Marine Information Technology (MOE), Xiamen University, 422 Siming Road South, Xiamen, FJ 361005, China
3Department of Geography and Environmental Management, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
Keywords: Mobile LiDAR, Tree Extraction, Shape Context, Segmentation, Point Cloud Abstract. This paper presents an automated algorithm for extracting 3D trees directly from 3D mobile light detection and ranging (LiDAR) data. To reduce both computational and spatial complexities, ground points are first filtered out from a raw 3D point cloud via blockbased elevation filtering. Off-ground points are then grouped into clusters representing individual objects through Euclidean distance clustering and voxel-based normalized cut segmentation. Finally, a model-driven method is proposed to achieve the extraction of 3D trees based on a pairwise 3D shape descriptor. The proposed algorithm is tested using a set of mobile LiDAR point clouds acquired by a RIEGL VMX-450 system. The results demonstrate the feasibility and effectiveness of the proposed algorithm.
Conference paper (PDF, 885 KB)


Citation: Yu, Y., Li, J., Guan, H., Zai, D., and Wang, C.: Automated Extraction of 3D Trees from Mobile LiDAR Point Clouds, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5, 629-632, doi:10.5194/isprsarchives-XL-5-629-2014, 2014.

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