Robust Context Free Segmentation of Unordered 3D Point Cloud Models

Accurate and rapidly produced 3D models of the as-built environment can be significant assets for a variety of civil engineering scenarios. Starting with a point cloud of a scene--generated using laser scanners or image-based reconstruction method--the user must first identify collections of points that belong to individual surfaces, and then fit surfaces and solid geometry objects appropriate for the analysis. When performed manually, this task often is prohibitively time consuming and, in response, several research groups recently have focused on developing methods for automating the modeling process. Because of the limitations of the data collection processes, as well as the complexity of as-built scenes, automated 3D modeling still presents many challenges. To overcome existing limitations, in this paper we propose a new region growing method for robust context-free segmentation of unordered point clouds based on geometrical continuities. In our method, only one parameter is required to be set by the user to account for the desired level of abstraction. Preliminary experimental results from two challenging scenes of the built environment demonstrate that our method can account for variability in point cloud density, surface roughness, curvature, and clutter within a single scene.