GRAR: Glass-induced Reflection Artifact Removal in LiDAR Point Clouds

2026-06-09

AI Summary

This work addresses the challenge of reflection artifacts in terrestrial laser scanning (TLS) point clouds caused by glass surfaces in urban environments, which severely degrade downstream tasks. The authors propose a unified two-stage framework: first, they integrate multimodal vision foundation models with geometric cues to accurately estimate and complete glass regions; second, they introduce the RE-LGGS descriptor, grounded in the physical geometry of reflections, which leverages multiscale structural features and directional consistency to effectively identify and remove reflection artifacts. Moving beyond conventional symmetry assumptions, the method incorporates multimodal priors and a physics-driven local-global geometric similarity model. Evaluated on multiple public TLS datasets, the approach significantly outperforms existing methods, achieving substantial improvements in both accuracy and robustness of artifact removal.

Abstract

Terrestrial Laser Scanning (TLS) point clouds captured in urban environments frequently suffer from glass-induced reflection artifacts, severely degrading downstream applications. Existing reflection artifact removal methods generally rely on ideal reflection symmetry assumptions, yet their performance is limited by inaccurate glass estimation and insufficient geometric representations. To address these issues, we propose a novel unified framework aimed at robust reflection artifact removal: In the first stage, we leverage a multi-modal vision foundation model to produce initial glass masks, which are then refined using geometric cues to achieve high-precision glass regions, followed by glass completion to recover missing regions caused by no-return measurements on transparent surfaces; In the second stage, we propose a physics-driven descriptor, termed Reflection-aware Local-Global Geometric Similarity (RE-LGGS), which is grounded in actual laser reflection geometry and jointly encodes multi-scale geometric structures and orientation consistency using PCA-based local shape representations, thereby significantly improving robustness against imperfect observations. Extensive experiments on multiple public TLS datasets demonstrate that our framework consistently outperforms state-of-the-art methods in reflection artifacts removal.

Problem

• LiDAR point clouds
• glass-induced reflection artifacts
• Terrestrial Laser Scanning
• reflection artifact removal

Innovation

• reflection artifact removal
• glass segmentation
• multi-modal foundation model
• physics-driven descriptor
• LiDAR point cloud