POINT CLOUD SMOOTH SAMPLING AND SURFACE RECONSTRUCTION BASED ON MOVING LEAST SQUARES

8 citations as recorded by crossref.

1. Designing Vector Fields on Smoothed Particle Hydrodynamics Using Divergence-Constrained Moving Least Squares J. Kim https://doi.org/10.15701/kcgs.2025.31.4.11
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3. StructuredLight-GNN: A graph neural network for end-to-end structured light 3D point cloud processing L. Liu et al. https://doi.org/10.1016/j.rio.2025.100943
4. Weld-seam identification and model reconstruction of remanufacturing blade based on three-dimensional vision O. Jing et al. https://doi.org/10.1016/j.aei.2021.101300
5. Optimising 3D point cloud semantic segmentation: ML and manual refinement in the UNESCO Saltaire Village V. Cotella et al. https://doi.org/10.1080/09613218.2025.2521751
6. Non-Destructive Surface Characterization Using Microscopic Imaging and Data Modeling M. Mączka et al. https://doi.org/10.3390/ma18184376
7. Learning Representation of Turbulent Vector Fields via Efficient Moving Least Squares Based on Monte Carlo Method J. Kim & J. Lee https://doi.org/10.1109/ACCESS.2025.3601241
8. Visualization of Vector Fields from Density Data Using Moving Least Squares Based on Monte Carlo Method J. Kim https://doi.org/10.15701/kcgs.2024.30.2.1