3D Change Detection Using Adaptive Thresholds Based on Local Point Cloud Density

In recent years, because of highly developed LiDAR (Light Detection and Ranging) technologies, there has been increasing demand for 3D change detection in urban monitoring, urban model updating, and disaster assessment. In order to improve the effectiveness of 3D change detection based on point clou...

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Bibliographic Details
Published in:ISPRS international journal of geo-information 2021-03, Vol.10 (3), p.127
Main Authors: Liu, Dan, Li, Dajun, Wang, Meizhen, Wang, Zhiming
Format: Article
Language:English
Subjects:
3D change detection
Accuracy
adaptive thresholds
Change detection
Density
Detection
Experiments
Lasers
Lidar
Mathematical analysis
Model updating
Performance evaluation
point clouds
point-based comparison
Three dimensional models
Thresholds
Unmanned aerial vehicles
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Summary:In recent years, because of highly developed LiDAR (Light Detection and Ranging) technologies, there has been increasing demand for 3D change detection in urban monitoring, urban model updating, and disaster assessment. In order to improve the effectiveness of 3D change detection based on point clouds, an approach for 3D change detection using point-based comparison is presented in this paper. To avoid density variation in point clouds, adaptive thresholds are calculated through the k-neighboring average distance and the local point cloud density. A series of experiments for quantitative evaluation is performed. In the experiments, the influencing factors including threshold, registration error, and neighboring number of 3D change detection are discussed and analyzed. The results of the experiments demonstrate that the approach using adaptive thresholds based on local point cloud density are effective and suitable.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi10030127