Cluster-wise removal of reflection artifacts in large-scale 3D point clouds using superpixel-based glass region estimation

Abstract

Large-scale 3D point cloud (LS3DPC) models captured by LiDAR scanners suffer from reflection artifacts caused by glass in real-world scenes. In this paper, we propose a reflection artifact removal algorithm for LS3DPC models using a panoramic image. We first partition the panoramic color image corresponding to an input LS3DPC model into superpixels, and cluster the points projected to each superpixel according to their geometric positions. We determine a superpixel with multiple point clusters as a glass region. We also detect and remove the virtual clusters of reflection artifacts by evaluating the symmetry and geometry scores. The experiment results show that the proposed algorithm detects the glass regions more faithfully and removes the virtual objects more completely compared with the existing method.