VO-SMOG: A VERSATILE, SMOOTH SEGMENT-BASED GROUND FILTER FOR POINT CLOUDS VIA MULTI-SCALE VOXELIZATION

8 citations as recorded by crossref.

1. Virtual ADA Compliance Assessment: Mimicking Digital Inclinometers to Measure Slopes within Point Clouds Y. Zhou et al. https://doi.org/10.1061/JSUED2.SUENG-1477
2. Investigating the Impact of Training Data Set Selection for Pole-Like Object Classification in Mobile Laser Scanning Data K. Kenney & E. Che https://doi.org/10.1061/JSUED2.SUENG-1604
3. Vo-Norvana : Versatile Framework for Efficient Segmentation of Large Point Cloud Data Sets E. Che & M. Olsen https://doi.org/10.1061/JCCEE5.CPENG-4979
4. Comparison of Virtual Curb Ramp Slope Assessment from Multiple Lidar Platforms C. Ogbeta et al. https://doi.org/10.1061/JSUED2.SUENG-1612
5. A Method for Accurate Absolute Georeferencing of Underwater Photogrammetric Data for Coral Reef Change Analysis S. Lambert et al. https://doi.org/10.2112/JCOASTRES-D-26TM1-00003.1
6. Towards intelligent ground filtering of large-scale topographic point clouds: A comprehensive survey N. Qin et al. https://doi.org/10.1016/j.jag.2023.103566
7. A Terrain-Feature-Aware Multiscale PTD for Airborne LiDAR Ground Filtering W. Ni et al. https://doi.org/10.1109/JSTARS.2026.3688518
8. SlideSim: 3D Landslide Displacement Monitoring through a Physics-Based Simulation Approach to Self-Supervised Learning A. Senogles et al. https://doi.org/10.3390/rs14112644