Automatic Recovery of Networks of Thin Structures

Meng Song, Daniel Huber

Abstract: Applications, such as construction monitoring and planning for renovations, require the accurate recovery of existing conditions of structures. Many types of infrastructure are primarily comprised of arbitrarily-shaped thin structures (e.g., Truss bridges, steel frame buildings under construction, and transmission towers), which existing automatic modeling methods are incapable of handling. To address this issue, this paper presents an approach to automatically recognize and model beams, planes, and joints from a 3D point cloud containing a complex network of thin structures, and to recover their topology. In our approach, each beam is evolved from a seed by matching and aligning the cross section images. This growing algorithm can model beams with arbitrary cross sections. By performing the algorithm on a point connectivity graph, we distinguish beams from joints and improve the algorithm's robustness to closely spaced objects. In parallel, planes and joints are also extracted and modeled. The connectivity graph of these primitives allows for a compact, object-level understanding of the entire structure. We demonstrate the capability and robustness of our approach on both synthetic and real datasets.