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Efficient and automatic plane detection approach for 3-D rock mass point clouds

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Abstract

The detection of planar regions from three-dimensional (3-D) laser scanning point clouds has become more and more significant in many scientific fields, including 3-D reconstruction, augmented reality and analysis of discontinuities. In rock engineering, planes extracted from rock mass point clouds are the foundational step to build 3-D numerical models of rock mass, which is significant in analysis of rock stability. In the past, several approaches have been proposed for detecting planes from TLS point clouds. However, these methods have difficulties in processing rock points because of the uniqueness of rock. This paper introduces a novel and efficient method for plane detection from 3-D rock mass point clouds. Firstly, after filtering the raw point clouds of rock mass acquired through laser scanning, the point cloud is split into some small voxels according to the specified resolution. Then, for the purpose of acquisition of high-quality growth units, an accurate coplanarity test process is used in each voxel. Meanwhile, the accurate neighborhood information can be built according to the result of coplanarity test. Finally, small voxels are clustered into a completed plane by region growing and the procedure of postprecessing. The performance of this method was tested in one icosahedron point cloud and three rock mass point clouds. Compared with the existing methods, the results demonstrate superior performance of our method in the field of plane detection.

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Acknowledgements

This work is supported by the Key Research Program of Frontier Sciences CAS (QYZDY-SSW-SYS004), Beijing Nova program (Z171100001117048), Beijing science and technology projectZ181100003818019, the Open Research Fund of Key Laboratory of Space Utilization, Chinese Academy of Sciences, the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA23090304), National Natural Science Foundation of China (61471338, 61802362), and Youth Innovation Promotion Association CAS (2015361).

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  1. University of Chinese Academy of Science, Beijing, China

    Liang Hu, Jun Xiao & Ying Wang

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  1. Liang Hu
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Correspondence to Jun Xiao.

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Hu, L., Xiao, J. & Wang, Y. Efficient and automatic plane detection approach for 3-D rock mass point clouds. Multimed Tools Appl 79, 839–864 (2020). https://doi.org/10.1007/s11042-019-08189-6

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