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Pipe-Run Extraction and Reconstruction from Point Clouds*
Rongqi Qiu1, Qian-Yi Zhou2, and Ulrich Neumann1
1University of Southern California, USA
2Stanford University, USA
Abstract. This paper presents automatic methods to extract and reconstruct 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 cylindric shapes, similarities reveal the inter-primitive relations intrinsic to industrial structures because of human design and construction. Statistical 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.
Electronic Supplementary Material:
LNCS 8691, p. 17 ff.
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© Springer International Publishing Switzerland 2014