CLASSIFICATION OF MLS POINT CLOUDS IN URBAN SCENES USING DETRENDED GEOMETRIC FEATURES FROM SUPERVOXEL-BASED LOCAL CONTEXTS

11 citations as recorded by crossref.

1. Strict rule-based automatic training data extraction using Mobile laser scanning in urban area Z. Ma et al. 10.1016/j.isprsjprs.2022.10.007
2. D-Net: A Density-Based Convolutional Neural Network for Mobile LiDAR Point Clouds Classification in Urban Areas M. Zaboli et al. 10.3390/rs15092317
3. Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review E. Che et al. 10.3390/s19040810
4. Classification of LiDAR Point Clouds Using Supervoxel-Based Detrended Feature and Perception-Weighted Graphical Model Y. Xu et al. 10.1109/JSTARS.2019.2951293
5. Toward Building and Civil Infrastructure Reconstruction From Point Clouds: A Review on Data and Key Techniques Y. Xu & U. Stilla 10.1109/JSTARS.2021.3060568
6. Visibility analysis for the occlusion detection and characterisation in street point clouds acquired with Mobile Laser Scanning J. Barros-Ribademar et al. 10.1080/10106049.2022.2032392
7. Pixel-level block classification and crack detection from 3D reconstruction models of masonry structures using convolutional neural networks D. Loverdos & V. Sarhosis 10.1016/j.engstruct.2024.118113
8. Three-Dimensional Reconstruction of Structural Surface Model of Heritage Bridges Using UAV-Based Photogrammetric Point Clouds Y. Pan et al. 10.3390/rs11101204
9. Merge-Swap Optimization Framework for Supervoxel Generation from Three-Dimensional Point Clouds Y. Xiao et al. 10.3390/rs12030473
10. OPTIMIZATION METHODS OF THE PAVEMENT MANAGEMENT SYSTEM OF BUDAPEST K. Almassy et al. 10.3846/jcem.2019.10925
11. Improvement of 3D LiDAR point cloud classification of urban road environment based on random forest classifier M. Mohamed et al. 10.1080/10106049.2022.2102218