A coupled optical–radiometric modeling approach to removing reflection noise in TLS data of urban areas

Point clouds, which are a fundamental type of 3D data, play an essential role in various applications like 3D reconstruction, autonomous driving, and robotics. However, point clouds generated via measuring the time-of-flight of emitted and backscattered laser pulses of TLS, frequently include false points caused by mirror-like reflective surfaces, resulting in degradation of data quality and fidelity. This study introduces an algorithm to eliminate reflection noise from TLS scan data. Our novel algorithm detects reflection planes by utilizing both geometric and physical characteristics to recognize reflection points according to optical reflection theory. Radiometric correction is applied to the raw laser intensity, after which reflective planes are extracted using a threshold. In the virtual points identification phase, these points are detected along the light propagation path, grounded on the specular reflection principle. Moreover, an improved feature descriptor, known as RE-LFSH, is employed to assess the similarity between two points in terms of reflection symmetry. We have adapted the LFSH feature descriptor to retain reflection features, mitigating interference from symmetrical architectural structures. Incorporating the Hausdorff feature distance into the algorithm fortifies its resistance to ghosting and deformations, thereby boosting the accuracy of virtual point detection. Additionally, to overcome the shortage of annotated datasets, a novel benchmark dataset named 3DRN, specifically designed for this task, is introduced. 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 areas by 57.03% and 31.80%, respectively. Our approach improves outlier detection by 9.17% and enhances accuracy by 5.65% compared to leading methods. You can access the 3DRN dataset at https://github.com/Tsuiky/3DRN.