A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Extracting Bridge Components from a Laser Scanning Point Cloud
A three-dimensional (3D) geometric model of a bridge plays an important role in inspection, assessment and management of the bridge. As most bridges were built after the second world war, 3D bridge models are rarely available. A recent development in laser scanning offers a cost-efficient method to capture dense, accurate 3D topographic data of the bridge. However, given the typical complexity of the bridge, a current workflow based commercial software to construct the bridge model still requires intensive labour work. This paper introduces a new approach to extract the point cloud of each surface of structural components of a slab/box beam bridge automatically in a sequential order from a superstructure to a substructure. The proposed method first employs a quadtree to decompose the point cloud of the bridge into two dimensional (2D) cells. Second, a kernel density estimation is used to separate a point cloud describing patches of surfaces within the cells. Subsequently, the cell- and voxel-based region growing are developed to segment patches within the cells/voxels for the superstructure and substructure, respectively. Moreover, knowledge of the bridge’s components (e.g. position, orientation, or shape) is introduced to allow the proposed method to identify criteria for filtering irrelevant objects, and to establish criteria for extracting the components. An experimental test shows the proposed method successfully extracts all surfaces of the bridge components.
Extracting Bridge Components from a Laser Scanning Point Cloud
A three-dimensional (3D) geometric model of a bridge plays an important role in inspection, assessment and management of the bridge. As most bridges were built after the second world war, 3D bridge models are rarely available. A recent development in laser scanning offers a cost-efficient method to capture dense, accurate 3D topographic data of the bridge. However, given the typical complexity of the bridge, a current workflow based commercial software to construct the bridge model still requires intensive labour work. This paper introduces a new approach to extract the point cloud of each surface of structural components of a slab/box beam bridge automatically in a sequential order from a superstructure to a substructure. The proposed method first employs a quadtree to decompose the point cloud of the bridge into two dimensional (2D) cells. Second, a kernel density estimation is used to separate a point cloud describing patches of surfaces within the cells. Subsequently, the cell- and voxel-based region growing are developed to segment patches within the cells/voxels for the superstructure and substructure, respectively. Moreover, knowledge of the bridge’s components (e.g. position, orientation, or shape) is introduced to allow the proposed method to identify criteria for filtering irrelevant objects, and to establish criteria for extracting the components. An experimental test shows the proposed method successfully extracts all surfaces of the bridge components.
Extracting Bridge Components from a Laser Scanning Point Cloud
Lecture Notes in Civil Engineering
Toledo Santos, Eduardo (editor) / Scheer, Sergio (editor) / Truong-Hong, Linh (author) / Lindenbergh, Roderik (author)
International Conference on Computing in Civil and Building Engineering ; 2020 ; São Paulo, Brazil
Proceedings of the 18th International Conference on Computing in Civil and Building Engineering ; Chapter: 50 ; 721-739
2020-07-14
19 pages
Article/Chapter (Book)
Electronic Resource
English
Point cloud , Segmentation , Cell-based segmentation , Bridge deficiencies , Bridge inspection , Bridge modelling , Bridge components , Slab/box bridge Engineering , Building Construction and Design , Cyber-physical systems, IoT , Data Engineering , Data Mining and Knowledge Discovery , Facility Management
Extracting Bridge Components from a Laser Scanning Point Cloud
| TIBKAT | 2021
Automatic Extracting Road Edges from Mobile Laser Scanner Point Cloud
| Springer Verlag | 2023
| European Patent Office | 2023