Automated Extraction of 3D Trees from Mobile LiDAR Point Clouds
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2014-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-5/629/2014/isprsarchives-XL-5-629-2014.pdf |
| Summary: | 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. |
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| ISSN: | 1682-1750 2194-9034 |