NDLI: Automated Extraction of 3D Trees from Mobile LiDAR Point Clouds

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Automated Extraction of 3D Trees from Mobile LiDAR Point Clouds

Content Provider	Semantic Scholar
Author	Yu, Yongtao Li, Jiaqiang Guan, Haiyan Zai, Dawei Wang, Cheng
Copyright Year	2014
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.
Starting Page	629
Ending Page	632
Page Count	4
File Format	PDF HTM / HTML
DOI	10.5194/isprsarchives-XL-5-629-2014
Alternate Webpage(s)	http://rssi.xmu.edu.cn/publications/2014-Yu-Li-Guan-Zai-Wang-isprs-archives.pdf
Alternate Webpage(s)	http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-5/629/2014/isprsarchives-XL-5-629-2014.pdf
Alternate Webpage(s)	https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-5/629/2014/isprsarchives-XL-5-629-2014.pdf
Alternate Webpage(s)	https://doi.org/10.5194/isprsarchives-XL-5-629-2014
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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