- Reflection removal for 3D point clouds
Large-scale 3D point clouds (LS3DPCs) captured by terrestrial LiDAR scanners often exhibit reflection artifacts by glasses, which degrade the performance of related computer vision techniques. In this paper, we propose an efficient reflection removal algorithm for LS3DPCs. We first partition the unit sphere into local surface patches which are then classified into the ordinary patches and the glass patches according to the number of echo pulses from emitted laser pulses. Then we estimate the glass region of dominant reflection artifacts by measuring the reliability. We also detect and remove the virtual points using the conditions of the reflection symmetry and the geometric similarity. We test the performance of the proposed algorithm on LS3DPCs capturing real-world outdoor scenes, and show that the proposed algorithm estimates valid glass regions faithfully and removes the virtual points caused by reflection artifacts successfully.
- Publications
| [1] | Jae-Seong Yun and Jae-Young Sim, “Reflection removal for large-scale 3D point clouds,” in Proc. IEEE CVPR, Salt Lake City, USA, June 2018. [more] |
| [2] | Jae-Seong Yun and Jae-Young Sim, “Virtual point removal for large-scale 3D point clouds with multiple glass planes,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 2, pp. 729-744, Feb. 2021. [more] |
| [3] | Jae-Seong Yun and Jae-Young Sim, “Cluster-wise removal of reflection artifacts in large-scale 3D point clouds using superpixel-based glass region estimation,” in Proc. IEEE ICIP, Taipei, Taiwan, 2019. |
| [4] | Oggyu Lee, Kyungdon Joo, Jae-Young Sim, “Learning-based reflection-aware virtual point removal for large-scale 3D point clouds,” in Proc.IEEE Robotics and Automation Letters, vol. 8, pp. 8510-8517, Dec. 2023. |
- Related Datasets
- UNIST LS3DPC Dataset [more]