GRAR: Glass-induced Reflection Artifact Removal in LiDAR Point Clouds
Researchers have developed a new framework called GRAR to address reflection artifacts in LiDAR point clouds, which often degrade data quality in urban environments. The system first uses a multi-modal vision foundation model to identify glass regions, then refines these masks with geometric cues and completes missing data. A novel physics-driven descriptor, RE-LGGS, further enhances accuracy by encoding geometric structures and orientation consistency, outperforming existing methods in experiments. AI
IMPACT Improves accuracy of LiDAR data processing, potentially benefiting autonomous driving and urban mapping.