Back to Search
Start Over
A Cluster-Based 3D Reconstruction System for Large-Scale Scenes.
- Source :
- Sensors (14248220); Mar2023, Vol. 23 Issue 5, p2377, 30p
- Publication Year :
- 2023
-
Abstract
- The reconstruction of realistic large-scale 3D scene models using aerial images or videos has significant applications in smart cities, surveying and mapping, the military and other fields. In the current state-of-the-art 3D-reconstruction pipeline, the massive scale of the scene and the enormous amount of input data are still considerable obstacles to the rapid reconstruction of large-scale 3D scene models. In this paper, we develop a professional system for large-scale 3D reconstruction. First, in the sparse point-cloud reconstruction stage, the computed matching relationships are used as the initial camera graph and divided into multiple subgraphs by a clustering algorithm. Multiple computational nodes execute the local structure-from-motion (SFM) technique, and local cameras are registered. Global camera alignment is achieved by integrating and optimizing all local camera poses. Second, in the dense point-cloud reconstruction stage, the adjacency information is decoupled from the pixel level by red-and-black checkerboard grid sampling. The optimal depth value is obtained using normalized cross-correlation (NCC). Additionally, during the mesh-reconstruction stage, feature-preserving mesh simplification, Laplace mesh-smoothing and mesh-detail-recovery methods are used to improve the quality of the mesh model. Finally, the above algorithms are integrated into our large-scale 3D-reconstruction system. Experiments show that the system can effectively improve the reconstruction speed of large-scale 3D scenes. [ABSTRACT FROM AUTHOR]
- Subjects :
- SMART cities
SUBGRAPHS
CAMERAS
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 23
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 162386332
- Full Text :
- https://doi.org/10.3390/s23052377