Abstract

This paper presents automatic methods to extract and recon- struct industrial site pipe-runs from large-scale point clouds. We observe three key characteristics in this modeling problem, namely, primitives, similarities, and joints. While primitives capture the dominant cylin- dric shapes, similarities reveal the inter-primitive relations intrinsic to industrial structures because of human design and construction. Statisti- cal analysis over point normals discovers primitive similarities from raw data to guide primitive fitting, increasing robustness to data noise and incompleteness. Finally, joints are automatically detected to close gaps and propagate connectivity information. The resulting model is more than a collection of 3D triangles, as it contains semantic labels for pipes as well as their connectivity.