POINTNET FOR THE AUTOMATIC CLASSIFICATION OF AERIAL POINT CLOUDS

10 citations as recorded by crossref.

1. Deep Ordinal Classification in Forest Areas Using Light Detection and Ranging Point Clouds A. Morales-Martín et al. 10.3390/s24072168
2. Automated bridge component recognition from point clouds using deep learning H. Kim et al. 10.1002/stc.2591
3. 3D point‐cloud spatial expansion by total least‐squares line fitting P. Dąbrowski & M. Zienkiewicz 10.1111/phor.12345
4. Evaluation of Class Distribution and Class Combinations on Semantic Segmentation of 3D Point Clouds With PointNet E. Barnefske & H. Sternberg 10.1109/ACCESS.2022.3233411
5. DAPNet++: density adaptive PointNet + + for airborne laser scanning data Z. Akbulut & F. Karsli 10.1007/s12145-024-01543-9
6. LiDAR Point Cloud Recognition of Overhead Catenary System with Deep Learning S. Lin et al. 10.3390/s20082212
7. Airborne Laser Scanning Point Cloud Classification Using the DGCNN Deep Learning Method E. Widyaningrum et al. 10.3390/rs13050859
8. A state-of-the-art review of automated extraction of rock mass discontinuity characteristics using three-dimensional surface models R. Battulwar et al. 10.1016/j.jrmge.2021.01.008
9. Object-level classification of vegetable crops in 3D LiDAR point cloud using deep learning convolutional neural networks R. Jayakumari et al. 10.1007/s11119-021-09803-0
10. Utilizing Deep Learning for the Automated Extraction of Rock Mass Features from Point Clouds R. Battulwar et al. 10.1007/s10706-024-02886-2