Acquisition Realism in Virtual Terrestrial Laser Scanning of IFC Bridge Models: A Screening Study of Boundary-Aware Synthetic-to-Real Transfer for Point Cloud Segmentation

Abstract

Terrestrial laser scanning (TLS) of bridges supports as-built reconstruction and digital-twin maintenance, but semantic segmentation of such scans is limited by annotation cost, and synthetic point clouds generated from building information models (BIM/IFC) are the established remedy. The virtual scanner behind such data is usually a single fixed design decision validated on synthetic test splits, leaving open whether such benchmarks can be trusted and which acquisition-realism effects justify their cost. This research decomposes virtual laser scanning of four real IFC bridge models into four switchable realism factors—occlusion, incidence-angle dropout, range-dependent noise, and per-station registration error—and measures each factor’s effect, in a leave-one-factor-out screening design, on transfer to twenty real TLS bridge scans. Three findings result. (1) Synthetic-test accuracy overstates real performance by up to +5.2 mean intersection-over-union (mIoU) points: the least realistic generator scores highest on its own test split while transferring no better, and realistic acquisition drives the overstatement towards zero. (2) Boundary-aware metrics, introduced here to infrastructure point clouds, separate component-interface failures that mIoU conflates. (3) After fine-tuning on fifteen real bridges, only realism pre-training improves significantly over training from scratch (+3.6 mIoU points, Welch t(4.0) = 3.48, p = 0.025, Hedges’ g = 2.27), with a same-direction gain at component interfaces. IFC-derived and real-scan supervision are quantifiably complementary.