Non-Learning BIM-Guided Weld Seam Localization from Partial Point Clouds for Robotic Welding of Steel Structures

Restricted viewpoints on construction sites often yield partial point clouds in which weld seams are partly or entirely occluded. This study proposes a non-learning BIM-guided method that localizes BIM-defined nominal weld seams by registering a known H-section beam–column joint model to the visible flange and web surfaces. Given an isolated point cloud and the acquisition side, finite analytic member surfaces and a BIM-derived Hesse atlas support multiple admissible initial poses. Each pose is refined using the same multiscale robust point-to-surface objective, and a deterministic terminal criterion selects the pose used to transfer nominal seam paths. Neither seam labels nor reconstruction of occluded geometry is required. On 402 synthetic observations, median seam RMSE, ADD-RMS, and relative rotation error were 1.110 mm, 1.249 mm, and 0.067 degrees, respectively; 89.05% had seam RMSE within 10 mm. On 180 laboratory observations, median point-to-surface distance and 5 mm attachment were 2.137 mm and 82.04%. Median warm processing times on a matched panel were 2.52 s and 3.18 s for synthetic and laboratory inputs, respectively. These findings support the applicability of the proposed method to nominal weld-seam localization under restricted partial-view conditions relevant to construction-site welding.