ITERATIVE CLOSEST POINT ALGORITHM FOR ACCURATE REGISTRATION OF COARSELY REGISTERED POINT CLOUDS WITH CITYGML MODELS

5 citations as recorded by crossref.

1. Global Navigation Satellite System-Aided Post-Simultaneous Localization and Mapping Correction of Lidar Maps: Modular Pipeline for Enhanced Global Accuracy F. Birhane et al. https://doi.org/10.1177/03611981261427755
2. Iterative closest point registration for fast point feature histogram features of a volume density optimization algorithm L. Wu et al. https://doi.org/10.1177/0020294019878869
3. L2M-Reg: Building-level uncertainty-aware registration of outdoor LiDAR point clouds and semantic 3D city models Z. Xu et al. https://doi.org/10.1016/j.isprsjprs.2026.02.005
4. Fully automatic smartphone-based photogrammetric 3D modelling of infant’s heads for cranial deformation analysis I. Barbero-García et al. https://doi.org/10.1016/j.isprsjprs.2020.06.013
5. A framework for road space extraction from point clouds and integration into 3D city models E. Tsiranidou et al. https://doi.org/10.1016/j.jag.2025.104803