| Title |
Performance comparison of noise control algorithms for the point cloud-based 3D models |
| Authors |
Dong-Gun Lee ; Han-Bin Park ; Kyu-Man Cho ; Tae-Hoon Kim |
| DOI |
https://dx.doi.org/10.6106/KJCEM.2026.27.2.023 |
| Keywords |
Point Cloud; Density; Statistical Outlier Removal; Radius Outlier Removal |
| Abstract |
As smart construction technologies continue to expand in the construction industry, the utilization of point cloud data (PCD) has increased significantly. Consequently, preprocessing to remove noise inherently included during data acquisition has become essential. Among the commonly used noise-removal techniques, Statistical Outlier Removal (SOR) and Radius Outlier Removal (ROR) are widely adopted; however, the performance of these algorithms can vary substantially depending on the density of the acquired PCD. Therefore, this study quantitatively analyzes the influence of PCD density on the performance of SOR and ROR by comparing removal-rate sensitivity to parameter variations and the extent of normal-point loss. The results show that both algorithms exhibit relatively stable behavior in high-density PCD, with small variations in removal rate and minimal normal-point loss. In contrast, low-density PCD responds sensitively to parameter changes, leading to a significant increase in normalpoint loss. Furthermore, SOR, which is based on global distance statistics, demonstrated more stable performance than ROR in low-density conditions, whereas ROR showed slightly better stability in high-density environments due to its local neighbor-based decision process. These findings suggest that more cautious parameter tuning is required when applying SOR and ROR to low-density PCD. The outcomes of this study are expected to serve as fundamental reference data for selecting appropriate algorithms and determining parameter settings according to PCD density characteristics in future preprocessing workflows. |