GRASS: Glass Reflection Artifact Suppression Strategy via Virtual Point Removal in LiDAR Point Clouds

In building measurement using terrestrial laser scanners (TLSs), acquired 3D point clouds (3DPCs) often contain significant reflection artifacts caused by reflective glass surfaces. Such reflection artifacts significantly degrade the performance of downstream applications. This study proposes a novel strategy, called GRASS, to remove these reflection artifacts. Specifically, candidate glass points are identified based on multi-echo returns caused by glass components. These potential glass regions are then refined through planar segmentation using geometric constraints. Then, we trace laser beam trajectories to identify the reflection affected zones based on the estimated glass planes and scanner positions. Finally, reflection artifacts are identified using dual criteria: (1) Reflection symmetry between artifacts and their source entities across glass components. (2) Geometric similarity through a 3D deep neural network. We evaluate the effectiveness of the proposed solution across a variety of 3DPC datasets and demonstrate that the method can reliably estimate multiple glass regions and accurately identify virtual points. Furthermore, both qualitative and quantitative evaluations confirm that GRASS outperforms existing methods in removing reflection artifacts by a significant margin.