Restoration of Individual Tree Missing Point Cloud Based on Local Features of Point Cloud

9Citations
Citations of this article
13Readers
Mendeley users who have this article in their library.

Abstract

LiDAR (Light Detection And Ranging) technology is an important means to obtain three-dimensional information of trees and vegetation. However, due to the influence of scanning mode, environmental occlusion and mutual occlusion between tree canopies and other factors, a tree point cloud often has different degrees of data loss, which affects the high-precision quantitative extraction of vegetation parameters. Aiming at the problem of a tree laser point cloud being missing, an individual tree incomplete point cloud restoration method based on local features of the point cloud is proposed. The L1-Median algorithm is used to extract key points of the tree skeleton, then the dominant direction of skeleton key points and local point cloud density are calculated, and the point cloud near the missing area is moved based on these features to gradually complete the incomplete point cloud compensation. The experimental results show that the above repair method can effectively repair the incomplete point cloud with good robustness and can adapt to the individual tree point cloud with different geometric structures and correct the branch topological connection errors.

Cite

CITATION STYLE

APA

Cao, W., Wu, J., Shi, Y., & Chen, D. (2022). Restoration of Individual Tree Missing Point Cloud Based on Local Features of Point Cloud. Remote Sensing, 14(6). https://doi.org/10.3390/rs14061346

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free