Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Detecting, Fitting, and Classifying Surface Primitives for Infrastructure Point Cloud Data
This paper presents a novel algorithm for detecting, fitting and classifying the embedded surface primitives from a point cloud dataset (PCD). Given a noisy infrastructure PCD, the final output of the algorithm consists of segmented surfaces, their estimated quadric models and corresponding surface classification. Initially, the PCD is down-sampled with a k-d tree structure then segmented via subspace learning. After pose recovery for each segmented group via singular value decomposition, a full quadric model is fit in MLESAC using the direct linear transform for parameter estimation. From the model parameters, the surface is classified from the rank, determinant, and eigenvalues of the parameter matrices. Finally, model merging is performed to simplify the results. A real-world PCD of a bridge is used to test the algorithm. The experimental validation of the algorithm demonstrates that the surface primitives are accurately estimated and classified.
Detecting, Fitting, and Classifying Surface Primitives for Infrastructure Point Cloud Data
This paper presents a novel algorithm for detecting, fitting and classifying the embedded surface primitives from a point cloud dataset (PCD). Given a noisy infrastructure PCD, the final output of the algorithm consists of segmented surfaces, their estimated quadric models and corresponding surface classification. Initially, the PCD is down-sampled with a k-d tree structure then segmented via subspace learning. After pose recovery for each segmented group via singular value decomposition, a full quadric model is fit in MLESAC using the direct linear transform for parameter estimation. From the model parameters, the surface is classified from the rank, determinant, and eigenvalues of the parameter matrices. Finally, model merging is performed to simplify the results. A real-world PCD of a bridge is used to test the algorithm. The experimental validation of the algorithm demonstrates that the surface primitives are accurately estimated and classified.
Detecting, Fitting, and Classifying Surface Primitives for Infrastructure Point Cloud Data
Zhang, G. (Autor:in) / Vela, P. A. (Autor:in) / Brilakis, I. (Autor:in)
ASCE International Workshop on Computing in Civil Engineering ; 2013 ; Los Angeles, California
Computing in Civil Engineering (2013) ; 589-596
24.06.2013
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Detecting, Fitting, and Classifying Surface Primitives for Infrastructure Point Cloud Data
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