Segmentation

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 work, we propose a new region growing method for robust context-free segmentation of unordered point clouds based on geometrical continuities. No assumptions are made about the intent of the segmentation it applies to a large context of engineering analysis. In our method, the user sets a single parameter, treated as a locally adaptive threshold, which accounts for the desired level of abstraction. Segmentation starts with a multi-scale feature detection, describing surface roughness and curvature around each 3D point, followed by seed finding and region growing steps. Preliminary experimental results from seven 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.

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