AN APPROACH TO EXTRACT MOVING OBJECTS FROM MLS DATA USING A VOLUMETRIC BACKGROUND REPRESENTATION

41 citations as recorded by crossref.

1. Efficient 3D Mapping and Modelling of Indoor Scenes with the Microsoft HoloLens: A Survey M. Weinmann et al. 10.1007/s41064-021-00163-y
2. A Review on Deep Learning Techniques for 3D Sensed Data Classification D. Griffiths & J. Boehm 10.3390/rs11121499
3. 3D Photogrammetry Point Cloud Segmentation Using a Model Ensembling Framework M. Chen et al. 10.1061/(ASCE)CP.1943-5487.0000929
4. Multi-level height maps-based registration method for sparse LiDAR point clouds in an urban scene B. Fang et al. 10.1364/AO.419746
5. Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation M. Ibrahim et al. 10.1109/ACCESS.2021.3062547
6. Moving event detection from LiDAR point streams H. Wu et al. 10.1038/s41467-023-44554-8
7. Automated extraction of expressway road surface from mobile laser scanning data T. Tran & C. Taweep 10.1007/s11771-020-4420-0
8. Mobile Radar Mapping—Subcentimeter SAR Imaging of Roads S. Palm et al. 10.1109/TGRS.2018.2842643
9. Data frame aware optimized Octomap-based dynamic object detection and removal in Mobile Laser Scanning data Z. Liu et al. 10.1016/j.aej.2023.05.014
10. Building Volumetric Beliefs for Dynamic Environments Exploiting Map-Based Moving Object Segmentation B. Mersch et al. 10.1109/LRA.2023.3292583
11. 3D CNN-Based Semantic Labeling Approach for Mobile Laser Scanning Data B. Nagy & C. Benedek 10.1109/JSEN.2019.2927269
12. Automated Localization and Classification of Expressway Pole‐Like Road Facilities from Mobile Laser Scanning Data T. Thanh Ha et al. 10.1155/2020/5016783
13. Dynamic Road Boundary Detection Using Sparse 3D-LiDAR in Complex Road and Environmental Conditions A. Gupta et al. 10.1177/03611981251342781
14. SSF-MOS: Semantic Scene Flow Assisted Moving Object Segmentation for Autonomous Vehicles T. Song et al. 10.1109/TIM.2024.3353835
15. Degeneracy‐Resistant LiDAR‐SLAM Algorithm Based on Geometric and Visual Features' Fusion D. Yang et al. 10.1002/rob.70026
16. An adaptive iterative reweighted filtering methodology for urban MLS dataset B. Suleymanoglu et al. 10.1080/14498596.2024.2350588
17. A breakline-preserving ground interpolation method for MLS data S. Xia et al. 10.1080/2150704X.2019.1670517
18. A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds C. Wen et al. 10.1016/j.isprsjprs.2018.10.007
19. Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods B. Gao et al. 10.1109/TITS.2021.3076844
20. FADOR: Fast and Accurate Dynamic Object Removal for Indoor Scenes X. Jin et al. 10.1109/TIM.2024.3427828
21. LiDAR-Driven Innovations in Pavement Distress Detection: A Review Y. Si et al. 10.1061/JCCEE5.CPENG-6662
22. Classification of LiDAR Point Clouds Using Supervoxel-Based Detrended Feature and Perception-Weighted Graphical Model Y. Xu et al. 10.1109/JSTARS.2019.2951293
23. An Enhanced Adaptive Monte Carlo Localization for Service Robots in Dynamic and Featureless Environments S. He et al. 10.1007/s10846-023-01858-7
24. Three Dimensional Change Detection Using Point Clouds: A Review A. Kharroubi et al. 10.3390/geomatics2040025
25. EmPointMovSeg: Sparse Tensor-Based Moving-Object Segmentation in 3-D LiDAR Point Clouds for Autonomous Driving-Embedded System Z. He et al. 10.1109/TCAD.2022.3172031
26. CSPC-Dataset: New LiDAR Point Cloud Dataset and Benchmark for Large-Scale Scene Semantic Segmentation G. Tong et al. 10.1109/ACCESS.2020.2992612
27. Change Detection of Urban Trees in MLS Point Clouds Using Occupancy Grids P. Hirt et al. 10.1007/s41064-021-00179-4
28. Deep Learning on Point Clouds and Its Application: A Survey W. Liu et al. 10.3390/s19194188
29. An Effective Way of Constructing Static Map Using 3-D LiDAR for Autonomous Navigation in Outdoor Environments W. Tang et al. 10.1109/JSEN.2023.3307398
30. Dynablox: Real-Time Detection of Diverse Dynamic Objects in Complex Environments L. Schmid et al. 10.1109/LRA.2023.3305239
31. Exploiting Structured CNNs for Semantic Segmentation of Unstructured Point Clouds from LiDAR Sensor M. Ibrahim et al. 10.3390/rs13183621
32. TUM-MLS-2016: An Annotated Mobile LiDAR Dataset of the TUM City Campus for Semantic Point Cloud Interpretation in Urban Areas J. Zhu et al. 10.3390/rs12111875
33. A Rapid Segmentation Method of Highway Surface Point Cloud Data Based on a Supervoxel and Improved Region Growing Algorithm W. Zhao et al. 10.3390/app14072852
34. MapCleaner: Efficiently Removing Moving Objects from Point Cloud Maps in Autonomous Driving Scenarios H. Fu et al. 10.3390/rs14184496
35. A Review of Dynamic Object Filtering in SLAM Based on 3D LiDAR H. Peng et al. 10.3390/s24020645
36. Classification and Change Detection in Mobile Mapping LiDAR Point Clouds M. Voelsen et al. 10.1007/s41064-021-00148-x
37. DOR-LINS: Dynamic Objects Removal LiDAR-Inertial SLAM Based on Ground Pseudo Occupancy Z. Wang et al. 10.1109/JSEN.2023.3310484
38. A training dataset for semantic segmentation of urban point cloud map for intelligent vehicles H. Song et al. 10.1016/j.isprsjprs.2022.02.007
39. Robust global registration of point clouds by closed-form solution in the frequency domain R. Huang et al. 10.1016/j.isprsjprs.2020.11.014
40. LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban Areas Z. Ye et al. 10.3390/ijgi9070450
41. Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features M. Hillemann et al. 10.3390/rs11161955