In respect of rock-mass engineering, the detection of planar structures from the rock-mass point clouds plays a crucial role in the construction of a lightweight numerical model, while the establishment of high-quality models relies on the accurate results of surface analysis. However, the existing techniques are barely capable to segment the rock mass thoroughly, which is attributed to the cluttered and unpredictable surface structures of the rock mass. This paper proposes a high-precision plane detection approach for 3D rock-mass point clouds, which is effective in dealing with the complex surface structures, thus achieving a high level of detail in detection. Firstly, the input point cloud is fast segmented to voxels using spatial grids, while the local coplanarity test and the edge information calculation are performed to extract the major segments of planes. Secondly, to preserve as much detail as possible, supervoxel segmentation instead of traditional region growing is conducted to deal with scattered points. Finally, a patch-based region growing strategy applicable to rock mass is developed, while the completed planes are obtained by merging supervoxel patches. In this paper, an artificial icosahedron point cloud and four rock-mass point clouds are applied to validate the performance of the proposed method. As indicated by the experimental results, the proposed method can make high-precision plane detection achievable for rock-mass point clouds while ensuring high recall rate. Furthermore, the results of both qualitative and quantitative analyses evidence the superior performance of our algorithm.
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.
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