POINTNET FOR THE AUTOMATIC CLASSIFICATION OF AERIAL POINT CLOUDS

9 citations as recorded by crossref.

1. Deep Ordinal Classification in Forest Areas Using Light Detection and Ranging Point Clouds A. Morales-Martín et al. https://doi.org/10.3390/s24072168
2. Automated bridge component recognition from point clouds using deep learning H. Kim et al. https://doi.org/10.1002/stc.2591
3. 3D point‐cloud spatial expansion by total least‐squares line fitting P. Dąbrowski & M. Zienkiewicz https://doi.org/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 https://doi.org/10.1109/ACCESS.2022.3233411
5. DAPNet++: density adaptive PointNet + + for airborne laser scanning data Z. Akbulut & F. Karsli https://doi.org/10.1007/s12145-024-01543-9
6. LiDAR Point Cloud Recognition of Overhead Catenary System with Deep Learning S. Lin et al. https://doi.org/10.3390/s20082212
7. Airborne Laser Scanning Point Cloud Classification Using the DGCNN Deep Learning Method E. Widyaningrum et al. https://doi.org/10.3390/rs13050859
8. Object-level classification of vegetable crops in 3D LiDAR point cloud using deep learning convolutional neural networks R. Jayakumari et al. https://doi.org/10.1007/s11119-021-09803-0
9. Utilizing Deep Learning for the Automated Extraction of Rock Mass Features from Point Clouds R. Battulwar et al. https://doi.org/10.1007/s10706-024-02886-2