COMPARISON OF TRADITIONAL AND MACHINE LEARNING BASE METHODS FOR GROUND POINT CLOUD LABELING

9 citations as recorded by crossref.

1. Review of Automatic Processing of Topography and Surface Feature Identification LiDAR Data Using Machine Learning Techniques Z. Gharineiat et al.
2. Deep learning for filtering the ground from ALS point clouds: A dataset, evaluations and issues N. Qin et al.
3. Integrating Elevation Frequency Histogram and Multi-Feature Gaussian Mixture Model for Ground Filtering of UAV LiDAR Point Clouds in Densely Vegetated Areas C. Liu et al.
4. Research on Fine-Scale Terrain Construction in High Vegetation Coverage Areas Based on Implicit Neural Representations Y. Zhang et al.
5. Enhancing point cloud semantic segmentation via scalable domain adaptation with LoRA-enabled PointNet++ M. Carós et al.
6. Integration of Physical Features and Machine Learning: CSF-RF Framework for Optimizing Ground Point Filtering in Vegetated Regions S. Zhang et al.
7. Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof F. Tarsha Kurdi et al.
8. Performance of unsupervised machine learning methods using chi-squared weights for LiDAR point cloud filtering in urban areas A. Sen et al.
9. Towards intelligent ground filtering of large-scale topographic point clouds: A comprehensive survey N. Qin et al.