A Radiometric Correction based Optical Modeling Approach to Removing Reflection Noise in TLS Point Clouds of Urban Scenes

TL;DR

This paper introduces a novel radiometric correction-based optical modeling approach to effectively remove reflection noise from TLS point clouds in urban scenes, significantly improving point cloud accuracy and robustness.

Contribution

The study develops a new reflection plane detection algorithm using geometry-optical models and adapts feature descriptors to better identify and eliminate reflective virtual points.

Findings

Improves precision and recall rates for reflective points by over 57% and 31%.

Achieves a 9.17% higher outlier detection rate compared to existing methods.

Enhances overall accuracy of TLS point clouds in urban environments.

Abstract

Point clouds are vital in computer vision tasks such as 3D reconstruction, autonomous driving, and robotics. However, TLS-acquired point clouds often contain virtual points from reflective surfaces, causing disruptions. This study presents a reflection noise elimination algorithm for TLS point clouds. Our innovative reflection plane detection algorithm, based on geometry-optical models and physical properties, identifies and categorizes reflection points per optical reflection theory. We've adapted the LSFH feature descriptor to retain reflection features, mitigating interference from symmetrical architectural structures. By incorporating the Hausdorff feature distance, the algorithm enhances resilience to ghosting and deformation, improving virtual point detection accuracy. Extensive experiments on the 3DRN benchmark dataset, featuring diverse urban environments with virtual TLS reflection noise, show our algorithm improves precision and recall rates for 3D points in reflective regions by 57.03\% and 31.80\%, respectively. Our method achieves a 9.17\% better outlier detection rate and 5.65\% higher accuracy than leading methods. Access the 3DRN dataset at (https://github.com/Tsuiky/3DRN).