Name
Abstract | ||
|---|---|---|
As many LiDAR point cloud processing steps, such as reconstruction, are often time- and memory-consuming, dividing LiDAR point clouds into subregions is common and necessary during preprocessing. However, the existing data dividing methods rely on tedious manual work or regular grids and result in oversegmentation around cutting lines. In this letter, we propose a new gap-based data dividing method for various LiDAR point clouds that can minimize the intersections between cutting lines and objects. The basic idea is to find a set of optimal paths that consist of gaps between objects as potential cutting lines. The experiments and comparisons in three data sets demonstrate that the proposed method is much better than the baseline method in terms visual inspection and cutting line quality. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2021.3063290 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Three-dimensional displays, Laser radar, Data mining, Roads, Trajectory, Task analysis, Feature extraction, Building blocks, data segmentation, grid-based, LiDAR preprocessing, point clouds | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaobo Xia | 1 | 0 | 0.34 |
| Sheng Nie | 2 | 2 | 3.47 |
| Pu Wang | 3 | 0 | 1.69 |
| Dong Chen | 4 | 0 | 4.06 |
| Sheng Xu | 5 | 507 | 71.47 |
| Cheng Wang | 6 | 3 | 7.89 |