A CAD-Prior-Guided Point Cloud Semantic Segmentation and Completion Method for Large-Span Spatial Truss Structures

To support design-consistent modeling of large-span trusses from field scans, this study aims to develop and evaluate a workflow guided by CAD priors for isolating load-bearing members and completing structural topology in severely occluded regions. A semantic segmentation algorithm based on voxel spatial indexing and two-stage neighborhood matching first uses the CAD model to discretize the search space and isolate main load-bearing trusses from cluttered backgrounds. On a manually labeled subset of 339,075 measured points from 12 representative regions, the segmentation stage achieves an F1-score of 85.65% and an IoU of 74.91%. A point cloud fusion and completion algorithm then combines distance-weighted iterative closest point registration with region-growing hierarchical mapping to reconstruct design-consistent point sets in shadowed node regions. The workflow is evaluated on the Zhongyuan International Convention and Exhibition Center. It achieves a normalized Chamfer distance of 0.53% and a normalized Hausdorff distance of 1.16% relative to the CAD reference. Under a 50 mm evaluation tolerance, the F-Score is 20.02%, more than twice the best traditional baseline, although the modest absolute value indicates limited strict-tolerance agreement. The output provides researchers, engineers, decision-makers, and asset managers with a traceable basis for digital-twin model preparation, inspection planning, and topology-aware documentation; CAD-derived points must nevertheless remain distinguishable from measured data in deformation monitoring and structural assessment.