Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Three-Dimensional Wireframe Reconstruction for Non-Manhattan-Shaped Point Clouds
https://orcid.org/0000-0002-9356-6155
https://orcid.org/0000-0002-5449-7599
This study proposes a feature relationship algorithm (FRA) to reconstruct three-dimensional wireframes of objects with non-Manhattan shapes using segmented point clouds. Instead of relying on extracting target boundaries, the FRA systematically identifies the vertex and edge nodes of objects and uses an innovative linking strategy to reconstruct a precise wireframe based on the point cloud geometry, even in the presence of data gaps. The FRA exhibits adaptability to various shapes, including curves, cones, pyramids, cylinders, octagonal prisms, and combinations. Validations on synthetic data provide valuable insights into the FRA’s parameter tuning and exceptional shape accuracy. On the other hand, the use of Light Detection and Ranging scans and benchmark data underscores the FRA’s fidelity in representing shapes from point clouds and demonstrates its improvement over baseline methods.
Three-Dimensional Wireframe Reconstruction for Non-Manhattan-Shaped Point Clouds
https://orcid.org/0000-0002-9356-6155
https://orcid.org/0000-0002-5449-7599
This study proposes a feature relationship algorithm (FRA) to reconstruct three-dimensional wireframes of objects with non-Manhattan shapes using segmented point clouds. Instead of relying on extracting target boundaries, the FRA systematically identifies the vertex and edge nodes of objects and uses an innovative linking strategy to reconstruct a precise wireframe based on the point cloud geometry, even in the presence of data gaps. The FRA exhibits adaptability to various shapes, including curves, cones, pyramids, cylinders, octagonal prisms, and combinations. Validations on synthetic data provide valuable insights into the FRA’s parameter tuning and exceptional shape accuracy. On the other hand, the use of Light Detection and Ranging scans and benchmark data underscores the FRA’s fidelity in representing shapes from point clouds and demonstrates its improvement over baseline methods.
Three-Dimensional Wireframe Reconstruction for Non-Manhattan-Shaped Point Clouds
https://orcid.org/0000-0002-9356-6155
https://orcid.org/0000-0002-5449-7599
This study proposes a feature relationship algorithm (FRA) to reconstruct three-dimensional wireframes of objects with non-Manhattan shapes using segmented point clouds. Instead of relying on extracting target boundaries, the FRA systematically identifies the vertex and edge nodes of objects and uses an innovative linking strategy to reconstruct a precise wireframe based on the point cloud geometry, even in the presence of data gaps. The FRA exhibits adaptability to various shapes, including curves, cones, pyramids, cylinders, octagonal prisms, and combinations. Validations on synthetic data provide valuable insights into the FRA’s parameter tuning and exceptional shape accuracy. On the other hand, the use of Light Detection and Ranging scans and benchmark data underscores the FRA’s fidelity in representing shapes from point clouds and demonstrates its improvement over baseline methods.
Three-Dimensional Wireframe Reconstruction for Non-Manhattan-Shaped Point Clouds
J. Comput. Civ. Eng.
Chuang, Tzu-Yi (Autor:in) / Ng, Hui-Yin (Autor:in) / Hsieh, Yo-Ming (Autor:in)
01.11.2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Reconstruction of Building LoD2 Wireframe Models Using Semantic Segmentation
| DOAJ | 2024
| British Library Online Contents | 2016