AOC: An adaptive oriented contraction method for automatic trace recognition of rock tunnel excavation face based on 3D point cloud

Trace recognition is essential for rock discontinuity characterization of tunnel excavation faces. Traditional methods of trace identification based on three-dimensional (3D) point cloud curvatures require manual fine-tuning for curvature detection and lack consistency with orientation grouping results. This paper proposes a new automatic method for trace identification from 3D point cloud. An adaptive vector method based on neighbor assignment is proposed to accurately generate both normal vectors and directional vectors on sharp points. A principal component analysis-based oriented contraction (PWI-OC) method is presented to extract point cloud skeletons with good iterative conformality. A sparse growing method is proposed to generate extensive trace segments. Two rock excavation face cases, from a mining tunnel and a railway tunnel, are adopted for analysis. The significance of adaptive normal vectors is validated for improving the quality of orientation grouping, and the iterative conformality of PWI-OC is validated to generate more accurate and robust trace skeletons than the traditional method. The results show that the proposed method can achieve a more accurate trace identification than traditional methods, consistent with orientation grouping results, robust to overlapping traces, and automates curvature point detection.