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

• Main Authors: Dan Liu, Dajun Li, Meizhen Wang, Zhiming Wang
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: ISPRS International Journal of Geo-Information
• Subjects: 3D change detection; adaptive thresholds; point-based comparison; point clouds
• Online Access: https://www.mdpi.com/2220-9964/10/3/127

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 <i>k</i>-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.