| Title |
Performance Analysis of Corner Extraction Methods for the 3D Point Cloud Building Models - Focusing on RANSAC and Convex Hull Approaches - |
| Authors |
Han-Bin Park ; Dong-Gun Lee ; Kyu-Man Cho ; Tae-Hoon Kim |
| DOI |
https://dx.doi.org/10.6106/KJCEM.2026.27.2.042 |
| Keywords |
Point Cloud Data Density; Corner Extraction; RANSAC; Convex Hul (C.H) |
| Abstract |
With the increasing use of point cloud data (PCD) for building facade inspection and digital-twin construction, the importance of reliable corner detection techniques has continued to grow. However, the density of PCD acquired in field environments varies significantly depending on sensor performance, scanning conditions, and accessibility, and the impact of such density variations on corner detection accuracy has not been sufficiently verified. To address this issue, this study constructed high-density and low-density PCD for the same building and quantitatively compared the corner detection performance of two representative point-extraction algorithms: RANSAC and Convex Hull. The results show that both methods yielded stable corner positions in high-density PCD, whereas low-density PCD exhibited accuracy degradation due to geometric distortion and point omission. These findings demonstrate that PCD density is a critical factor influencing the reliability of corner detection and highlight the need for further research to improve feature extraction robustness under low-density scanning conditions. |