Reflection Removal of 3D Point Clouds for Realistic 3D Scene Reconstruction

Abstract

With an advent of LiDAR scanners, the Large-scale 3D point clouds (LS3DPCs) captured by terrestrial LiDAR scanners or dynamic 3D point cloud captured by real-time LiDAR scanners are widely used. However, the captured 3D point clouds often include virtual points which are generated by glass reflection. The virtual points may degrade the performance of various computer vision techniques when applied to 3D point clouds. In this dissertation, we propose virtual point removal algorithms for static 3D point clouds and dynamic 3D point clouds captured by LiDAR scanners. We first propose an efficient reflection removal algorithm for static LS3DPCs when there is a single pane of glass. 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. Then, we propose a virtual point removal algorithm for LS3DPCs with multiple glass planes. We first estimate multiple glass regions by modeling the reliability with respect to each glass plane, respectively, such that the regions are assigned high reliability when they have multiple echo pulses for each emitted laser pulse. Then we detect each point whether it is a virtual point or not. For a given point, we recursively traverse all the possible trajectories of reflection, and select the optimal trajectory which provides a point with a similar geometric feature to a given point at the symmetric location. We evaluate the performance of the proposed algorithm on various LS3DPC models with diverse numbers of glass planes. Experimental results show that the proposed algorithm estimates multiple glass regions faithfully and detects the virtual points successfully. Moreover, we also show that the proposed algorithm yields a much better performance of reflection artifact removal compared with the existing method qualitatively and quantitatively. Finally, we propose a novel virtual point removal algorithm for dynamic 3D point clouds. We first estimate the glass planes using specular reflection characteristics of glass. We collect local maxima points for every frames and estimate glass planes using the relationship between intensity and the angle of incidence for laser pulse. To remove virtual points more clearly, we cluster 3D point clouds and then, recursively estimate all possible trajectories of laser pulses and remove the clusters when there are reflections in the most reliable trajectory. Experimental results show that the proposed algorithm successfully estimate glass planes, and detect and remove virtual points. The main contribution of this dissertation is the removal of reflection artifacts in LS3DPCs and dynamic 3D point clouds. In first chapter, we analyze the characteristic of received echo pulses for terrestrial LiDAR scanners and estimate glass regions and glass planes. Also, we investigate the symmetry relation of corresponding real and virtual points. In second chapter, we propose recursive trajectory estimation methods to detect and remove reflection artifacts. We also perform quantitative evaluation of proposed methods. In last chapter, we analyze the relationship between intensity and the angle of incidence for the received echo pulse to estimate glass plane for dynamic 3D point clouds captured by real-time LiDAR scanners. Then, we apply cluster-wise removal of reflection artifacts to get clear reflection-free 3D point clouds.