Rock discontinuity extraction from 3D point clouds using pointwise clustering algorithm

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Recognizing discontinuities within rock masses is a critical aspect of rock engineering. The development of remote sensing technologies has significantly enhanced the quality and quantity of the point clouds collected from rock outcrops. In response, we propose a workflow that balances accuracy and efficiency to extract discontinuities from massive point clouds. The proposed method employs voxel filtering to downsample point clouds, constructs a point cloud topology using K-d trees, utilizes principal component analysis to calculate the point cloud normals, and employs the pointwise clustering (PWC) algorithm to extract discontinuities from rock outcrop point clouds. This method provides information on the location and orientation (dip direction and dip angle) of the discontinuities, and the modified whale optimization algorithm (MWOA) is utilized to identify major discontinuity sets and their average orientations. Performance evaluations based on three real cases demonstrate that the proposed method significantly reduces computational time costs without sacrificing accuracy. In particular, the method yields more reasonable extraction results for discontinuities with certain undulations. The presented approach offers a novel tool for efficiently extracting discontinuities from large-scale point clouds.