Segmentation of building point cloud models including detailed architectural/structural features and MEP systems

Accurate and rapidly produced 3D models of the as-built environment can be significant assets for a variety of Engineering scenarios. Starting with a point cloud of a scene - generated using laser scanners or image-based reconstruction methods - 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 is often prohibitively time consuming and, in response, several research groups have recently focused on developing methods for automating the modeling process. Due to 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, the user sets a single parameter which accounts for the desired level of abstraction. We treat this parameter as a locally adaptive threshold to account for local context. Our method of segmentation starts with a multi-scale feature detection, describing surface roughness and curvature around each 3D point, and is followed by seed finding and region growing steps. Experimental results from seven challenging point clouds 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.