Processing tree point clouds using Gaussian Mixture Models

24 citations as recorded by crossref.

1. SimpleTree —An Efficient Open Source Tool to Build Tree Models from TLS Clouds J. Hackenberg et al. https://doi.org/10.3390/f6114245
2. Semi-automatic extraction of liana stems from terrestrial LiDAR point clouds of tropical rainforests S. Krishna Moorthy et al. https://doi.org/10.1016/j.isprsjprs.2019.05.011
3. Automatic Detection and Parameter Estimation of Trees for Forest Inventory Applications Using 3D Terrestrial LiDAR A. Aijazi et al. https://doi.org/10.3390/rs9090946
4. A Deep Learning Time Series Approach for Leaf and Wood Classification from Terrestrial LiDAR Point Clouds T. Han & G. Sánchez-Azofeifa https://doi.org/10.3390/rs14133157
5. SegmentedForests: a labelled dataset of terrestrial LiDAR point clouds for semantic segmentation of forests D. Laino et al. https://doi.org/10.1093/forestry/cpaf062
6. A new approach to retrieve leaf normal distribution using terrestrial laser scanners S. Jin et al. https://doi.org/10.1007/s11676-015-0204-z
7. Analyzing the Vertical Distribution of Crown Material in Mixed Stand Composed of Two Temperate Tree Species O. Martin-Ducup et al. https://doi.org/10.3390/f9110673
8. WLC-Net: A Robust and Fast Deep Learning Wood–Leaf Classification Method H. Li et al. https://doi.org/10.3390/f16030513
9. Highly Accurate Tree Models Derived from Terrestrial Laser Scan Data: A Method Description J. Hackenberg et al. https://doi.org/10.3390/f5051069
10. Separating Tree Photosynthetic and Non-Photosynthetic Components from Point Cloud Data Using Dynamic Segment Merging D. Wang et al. https://doi.org/10.3390/f9050252
11. Individual Tree Canopy Parameters Estimation Using UAV-Based Photogrammetric and LiDAR Point Clouds in an Urban Park E. Ghanbari Parmehr & M. Amati https://doi.org/10.3390/rs13112062
12. Terrestrial lidar reveals new information about habitats provided by large old trees A. Holland et al. https://doi.org/10.1016/j.biocon.2024.110507
13. Non Destructive Method for Biomass Prediction Combining TLS Derived Tree Volume and Wood Density J. Hackenberg et al. https://doi.org/10.3390/f6041274
14. The utility of terrestrial photogrammetry for assessment of tree volume and taper in boreal mixedwood forests C. Mulverhill et al. https://doi.org/10.1007/s13595-019-0852-9
15. Modelling and Design of Habitat Features: Will Manufactured Poles Replace Living Trees as Perch Sites for Birds? A. Holland et al. https://doi.org/10.3390/su15097588
16. A hybrid approach for enhanced tree volume estimation of complex trees using terrestrial LiDAR M. Ali et al. https://doi.org/10.1080/15481603.2025.2474836
17. Improved Supervised Learning-Based Approach for Leaf and Wood Classification From LiDAR Point Clouds of Forests S. Krishna Moorthy et al. https://doi.org/10.1109/TGRS.2019.2947198
18. LiDAR Point Clouds to 3-D Urban Models$:$ A Review R. Wang et al. https://doi.org/10.1109/JSTARS.2017.2781132
19. Extraction of Liana Stems Using Geometric Features from Terrestrial Laser Scanning Point Clouds T. Han & G. Sánchez-Azofeifa https://doi.org/10.3390/rs14164039
20. Separating Leaf and Wood Points in Terrestrial Laser Scanning Data Using Multiple Optimal Scales J. Zhou et al. https://doi.org/10.3390/s19081852
21. Extraction of tubular shapes from dense point clouds and application to tree reconstruction from laser scanned data J. Ravaglia et al. https://doi.org/10.1016/j.cag.2017.05.016
22. Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data J. Ravaglia et al. https://doi.org/10.3390/f10070599
23. Terrestrial laser scanning to quantify above-ground biomass of structurally complex coastal wetland vegetation C. Owers et al. https://doi.org/10.1016/j.ecss.2018.02.027
24. Automated Wood and Leaf Separation: A Comprehensive Review on Advanced Methods and Forestry Applications T. Vu et al. https://doi.org/10.1109/ACCESS.2026.3709955