基于改进Alpha-shape的超高钢桥塔点云截面轮廓提取方法研究OA

The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extrac-tion of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,and tem-perature fluctuations can compromise the accuracy of con-tour extraction.To address these limitations,an improved Alpha-shape-based point cloud contour extraction method is proposed.The proposed approach uses a hierarchical strat-egy to process three-dimensional laser scanning point clouds.The processed data are then subjected to curvature-adaptive voxel filtering to reduce acquisition noise.In addi-tion,an enhanced iterative closest point(ICP)variant with correspondence validation accurately aligns the discrete point cloud segments.The proposed curvature-responsive Alpha-shape framework enables multiscale contour delinea-tion through topology-adaptive threshold modulation,which resolves boundary ambiguities in geometrically complex cross-sections.The method was experimentally validated us-ing field-acquired measurement datasets from the Zhangjing-gao Yangtze River Bridge tower segments,confirming its capability to reconstruct noncanonical cross-sectional geom-etries.Three contour extraction methods,including Poisson reconstruction,the conventional Alpha-shape algorithm,and random sample consensus with ICP(RANSAC-ICP),were compared to evaluate the performance of the proposed Alpha-shape algorithm.The results demonstrate that the proposed method achieves superior contour extraction accu-racy and data reduction efficiency,highlighting its effective-ness in contour extraction tasks.