A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Automatic Generation of As-Built Geometric Civil Infrastructure Models from Point Cloud Data
Converting remote sensing point cloud data (PCD) into solid CAD models consisting of civil infrastructure components is a crucial step in generating the as-built building information models. Previous research has enabled automatic generation of surface primitives from raw PCDs. However, the fully automatic conversion from surface primitives to infrastructure component models remains an unsolved problem. In this work, an automatic and linear-runtime approach is presented which generates the as-built infrastructure component models by recognizing the solid CAD entities and learning the infrastructure component labels from the fitted surface primitives. The algorithm utilizes a decision tree with the following decision variables: the type, parametric model, orientation, and mutual geometric relations of the fitted surface primitives. The decision tree is trained with easily generated synthetic data and is applied to query real-world data with complexity O (1). The output of the solid entities includes cuboid, cylinder, and ball, and the infrastructure component labels (such as columns, caps, deck, and beams). The algorithm is tested with various PCDs modeling real bridges.
Automatic Generation of As-Built Geometric Civil Infrastructure Models from Point Cloud Data
Converting remote sensing point cloud data (PCD) into solid CAD models consisting of civil infrastructure components is a crucial step in generating the as-built building information models. Previous research has enabled automatic generation of surface primitives from raw PCDs. However, the fully automatic conversion from surface primitives to infrastructure component models remains an unsolved problem. In this work, an automatic and linear-runtime approach is presented which generates the as-built infrastructure component models by recognizing the solid CAD entities and learning the infrastructure component labels from the fitted surface primitives. The algorithm utilizes a decision tree with the following decision variables: the type, parametric model, orientation, and mutual geometric relations of the fitted surface primitives. The decision tree is trained with easily generated synthetic data and is applied to query real-world data with complexity O (1). The output of the solid entities includes cuboid, cylinder, and ball, and the infrastructure component labels (such as columns, caps, deck, and beams). The algorithm is tested with various PCDs modeling real bridges.
Automatic Generation of As-Built Geometric Civil Infrastructure Models from Point Cloud Data
Zhang, G. (author) / Vela, P. A. (author) / Brilakis, I. (author)
2014 International Conference on Computing in Civil and Building Engineering ; 2014 ; Orlando, Florida, United States
2014-06-17
Conference paper
Electronic Resource
English
Automatic Generation of As-Built Geometric Civil Infrastructure Models from Point Cloud Data
| British Library Conference Proceedings | 2014
| British Library Online Contents | 2015