In rock engineering, the rock point cloud data scanned by a laser scanner always contains holes because of the scanning measurement angle, shadow, occlusion of obstacles, and other factors, which will affect the result of subsequent 3D reconstruction. The existing filling methods mainly focus on the regular point cloud data, and the point cloud hole is filled according to the neighborhood information of the holes, and the experiment result of rock point cloud holes detection and filling are not effective and low efficiency. In this paper, we propose an algorithm for detecting and filling rock point cloud holes based on the plane extraction leverage the characteristics of rock point cloud data. Firstly, an optimized region growing algorithm is applied to extract the plane of the rock point cloud. Then, we traverse all point clouds and retrieve their K-neighborhood point sets. These points are mapped to the corresponding plane, and we calculate the neighborhood angle to detect holes. Finally, we classify the point cloud holes according to the number of corresponding planes of the boundary point set, and the point cloud holes are filled by adding sampling points on the corresponding planes. Our algorithm realizes the process of denoising and plane fitting of point cloud data by plane extraction, simplifys the subsequent hole filling process and reduces the time complexity. Experimental results demonstrate that our algorithm has a higher accuracy of detection and filling, higher operation efficiency, and better filling result for rock-mass point cloud compared to the state-of-the-art approaches.
MA Zhaoyue
,
XIAO Jun
,
WANG Ying
. 3D rock mass point cloud holes detection and filling method based on plane extraction[J]. Journal of University of Chinese Academy of Sciences, 2022
, 39(5)
: 668
-676
.
DOI: 10.7523/j.ucas.2020.0032
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