Rigorous Density Correction Model for Single-Scan TLS Point Clouds

Abstract

Terrestrial laser scanning (TLS) can acquire high-precision and high-resolution 3D point clouds of the scanned targets, and the common technical challenge is how to accurately and efficiently process the massive unorganized point clouds. Density is one of the most fundamental and significant quantities that can be derived for each point by counting the number of neighbors in a given 3D space. It contains key information of the target geometric features and thus plays an important role in TLS data processing (e.g., classification and feature extraction). However, density usually varies remarkably for different scenes with varied scan geometry (e.g., distance and incidence angle), scan resolution (e.g., horizontal and vertical angular resolutions), and object geometry (e.g., slope, size, and spatial attitude), thereby greatly hindering its full capability in reflecting the geometric discrepancies of different targets. The density must be corrected before reliably used as a proxy for target geometric features. In this study, a generalized rigorous model is proposed to correct the density of single-scan TLS point clouds based on the mathematically deduced relation between density and related influencing factors. The superiority of the proposed model over existing models is that it is rigorously developed based on the instrumental scanning principles and target spatial geometry and can be used for different instruments, scanning scenes, targets, and scanning parameters. The proposed model is verified by a series of indoor quantitative control experiments and three natural scenes with totally different instruments and scanning conditions. The results show that the proposed model can accurately simulate the density variation at different scanning geometries with an average correlation coefficient of 0.9957 between the calculated and actual densities. The effects of all influencing factors on density are significantly removed in complex natural scenes by the proposed model where the coefficient of variation of the density from a homogeneous surface after correction is reduced by 72.31%, on the average. The proposed model exhibits good performance in terms of the feasibility, effectiveness, and generality and can derive a corrected density value that is a proxy of target geometric size. Additionally, the proposed model can be conducted on each scan individually before the co-registration in the actual TLS campaign with a number of single scans, which has tremendous application value in facilitating TLS data interpretation.