A Hierarchical Geometry-Driven Framework for Instance Segmentation Within Junction Regions in Steel Grid Structure Point Clouds

Abstract

Terrestrial laser scanning (TLS) point clouds are increasingly used for monitoring steel grid structures, and accurate instance segmentation is central to their processing. In complex environments, segmenting junction regions is challenging owing to multi-member geometry and incomplete sampling. Existing approaches frequently depend on prior information such as design drawings or Building Information Modeling (BIM), which limits generality and offers few solutions when model priors are unavailable. A hierarchical spherical coordinate segmentation with dual-sphere center refinement method (HSC-DCR) is proposed for geometry-driven junction region instance segmentation. The method uses radial connectivity. A segmentation origin is first located via a grid search driven by directional convergence evaluation, and a spherical coordinate system is then established for initial angular domain clustering. Subsequently, topological correction is guided by multi-dimensional indicators, and instance refinement is achieved through dual-sphere center optimization. The process is geometry-driven and independent of design models. Experiments on a laser-scanned stadium point cloud, covering 22 junction-region types and 521 instances, achieve an F1-score of 0.948 and mIoU of 85.5% under an instance-matched evaluation protocol. The results show that HSC-DCR can reliably obtain node and member instances from TLS point clouds without relying on drawings or BIM, supporting TLS-based monitoring of steel grid structures.