Efficient Lightweight Surface Reconstruction Method from Rock-Mass Point Clouds

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 5; p. 1200
• Main Authors: Yu, Dongbo, Xiao, Jun, Wang, Ying
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2022
• Subjects: Algorithms; Data compression; Efficiency; Image reconstruction; Integer programming; Lightweight; Mathematical models; Methods; Noise; Numerical models; point cloud; Remote sensing; Robustness (mathematics); rock mass; Rock masses; Rocks; Segmentation; Stability analysis; surface reconstruction; Texture recognition
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14051200

As for rock numerical calculation and stability analysis, it is essential to build a numerical model of rock mass with concise and accurate structure information through the three-dimensional surface reconstruction of rock-mass point clouds. However, the current research on lightweight surface reconstruction of non-artificial objects is very limited. In this paper, an efficient lightweight surface reconstruction method for rock-mass point clouds is proposed. Firstly, the input point cloud is segmented to obtain the plane primitives. In this process, the recognition of texture information and the complete over-segmentation of effective information play a vital role in the high-precision segmentation of rock surfaces. Secondly, the boundaries of all planes are reorganized according to the obvious connectivity in the segmentation results, so as to realize the assembly of the model, while solving all collision problems. Finally, an integer programming model is constructed to screen the best closure scheme of each plane, thus ensuring the best outcome of the reconstruction. In this study, seven groups of natural rock-mass point clouds are used to validate the proposed method. As suggested by the experimental results, this algorithm is effective in compressing the point cloud data of rock mass, to generate a watertight numerical model that can be directly used for simulation calculation. In addition, this method has strong robustness to noise and can effectively deal with highly corrupted rock-mass point cloud data.