A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Automatic segmentation and classification of BIM elements from point clouds
Abstract Laser techniques are widely used to perform topographic building surveys by providing massive information and point clouds comprised of millions of points in seconds. Point clouds allow the creation of 3D models that represent information of significant importance to the AEC/FM (Architecture, Engineering, Construction, and Facilities Management) domain. However, few tools exist related to the automatic modelling of point clouds. We present a method to automatically segment, classify, and model point clouds that were tested with two point clouds acquired via static and dynamic laser techniques. This approach generated accurate 3D surfaces of building elements, including floors, ceilings, walls columns, and content. A future study will involve transferring the 3D surfaces into Building Information Model elements.
Graphical abstract Display Omitted
Highlights The presented approach automatically segments unstructured point clouds and creates 3D surfaces. The algorithm is capable to run data both from static and dynamic terrestrial LiDAR (Light Detection and Ranging). ABM-indoor detects elements with a goodness of more than 90%. This algorithm reduces manual editing by more than a third. The 3D models obtained with ABM-indoor can be used for different applications.
Automatic segmentation and classification of BIM elements from point clouds
Abstract Laser techniques are widely used to perform topographic building surveys by providing massive information and point clouds comprised of millions of points in seconds. Point clouds allow the creation of 3D models that represent information of significant importance to the AEC/FM (Architecture, Engineering, Construction, and Facilities Management) domain. However, few tools exist related to the automatic modelling of point clouds. We present a method to automatically segment, classify, and model point clouds that were tested with two point clouds acquired via static and dynamic laser techniques. This approach generated accurate 3D surfaces of building elements, including floors, ceilings, walls columns, and content. A future study will involve transferring the 3D surfaces into Building Information Model elements.
Graphical abstract Display Omitted
Highlights The presented approach automatically segments unstructured point clouds and creates 3D surfaces. The algorithm is capable to run data both from static and dynamic terrestrial LiDAR (Light Detection and Ranging). ABM-indoor detects elements with a goodness of more than 90%. This algorithm reduces manual editing by more than a third. The 3D models obtained with ABM-indoor can be used for different applications.
Automatic segmentation and classification of BIM elements from point clouds
Romero-Jarén, R. (author) / Arranz, J.J. (author)
2021-01-18
Article (Journal)
Electronic Resource
English
BIM , LiDAR , Point cloud , 2D/3D , Automatism
Material augmented semantic segmentation of point clouds for building elements
| Wiley | 2024