Reflection removal for large-scale 3D point clouds

Abstract

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.

Results of Glass Estimation

Estimation of glass regions associated with dominant reflection artifacts. In each subfigure, a color panorama image and the resulting reliability distribution is shown in top and bottom, respectively.

Results of Virtual Point Detection

Results of reflection removal for LS3DPCs. In each subfigure, the left column shows an input LS3DPC model, the middle column visualizes the estimated glass regions (yellow) and the detected virtual points (red), and the right column shows the resulting LS3DPC model where the virtual points are removed.

Supplementary Material

Publication

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.