Accurate three-dimensional (3D) data from indoor spaces are of high importance for various applications in construction, indoor navigation and real estate management. Mobile scanning techniques are offering an efficient way to produce point clouds, but with a lower accuracy than the traditional terrestrial laser scanning (TLS). In this paper, we first tackle the problem of how the quality of a point cloud should be rigorously evaluated. Previous evaluations typically operate on some point cloud subset, using a manually-given length scale, which would perhaps describe the ranging precision or the properties of the environment. Instead, the metrics that we propose perform the quality evaluation to the full point cloud and over all of the length scales, revealing the method precision along with some possible problems related to the point clouds, such as outliers, over-completeness and misregistration. The proposed methods are used to evaluate the end product point clouds of some of the latest methods. In detail, point clouds are obtained from five commercial indoor mapping systems, Matterport, NavVis, Zebedee, Stencil and Leica Pegasus: Backpack, and three research prototypes, Aalto VILMA , FGI Slammer and the Würzburg backpack. These are compared against survey-grade TLS point clouds captured from three distinct test sites that each have different properties. Based on the presented experimental findings, we discuss the properties of the proposed metrics and the strengths and weaknesses of the above mapping systems and then suggest directions for future research.

Detailed modeling of floodplain flows and associated processes requires data on mixed, heterogeneous vegetation at river reach scale, though the collection of vegetation data is typically limited in resolution or lack spatial information. This study investigates physically-based characterization of mixed floodplain vegetation by means of terrestrial laser scanning (TLS). The work aimed at developing an approach for deriving the characteristic reference areas of herbaceous and foliated woody vegetation, and estimating the vertical distribution of woody vegetation. Detailed experimental data on vegetation properties were gathered both in a floodplain site for herbaceous vegetation, and under laboratory conditions for 2–3 m tall trees. The total plant area (A_tot) of woody vegetation correlated linearly with the TLS-based voxel count, whereas the A_tot of herbaceous vegetation showed a linear correlation with TLS-based vegetation mean height. For woody vegetation, 1 cm voxel size was found suitable for estimating both the A_tot and its vertical distribution. A new concept was proposed for deriving A_tot for larger areas from the point cloud attributes of small sub-areas. The results indicated that the relationships between the TLS attributes and A_tot of the sub-areas can be derived either by mm resolution TLS or by manual vegetation sampling.

Mobile mapping systems (MMSs) are used for mapping topographic and urban features which are difficult and time consuming to measure with other instruments. The benefits of MMSs include efficient data collection and versatile usability. This paper investigates the data processing steps and quality of a boat-based mobile mapping system (BoMMS) data for generating terrain and vegetation points in a river environment. Our aim in data processing was to filter noise points, detect shorelines as well as points below water surface and conduct ground point classification. Previous studies of BoMMS have investigated elevation accuracies and usability in detection of fluvial erosion and deposition areas. The new findings concerning BoMMS data are that the improved data processing approach allows for identification of multipath reflections and shoreline delineation. We demonstrate the possibility to measure bathymetry data in shallow (0–1 m) and clear water. Furthermore, we evaluate for the first time the accuracy of the BoMMS ground points classification compared to manually classified data. We also demonstrate the spatial variations of the ground point density and assess elevation and vertical accuracies of the BoMMS data.

High quality sedimentary measurements are required for studying fluvial geomorphology and hydrological processes e.g., flood and river dynamics. Mobile laser scanning (MLS) currently provides the opportunity to achieve high precision measurements of coarse fluvial sediments in a large survey area. Our study aims to investigate the capability of single-track MLS data for individual particle-based sediment modeling. Individual particles are firstly detected and delineated from a digital surface model (DSM) that is generated from the MLS data. 3D MLS points of each detected individual particle are then extracted from the point cloud. The grain size and the sphericity as well as the orientation of each individual particle are estimated based on the extracted MLS points. According to the evaluations conduced in the paper, it is possible to detect and to model sediment particles above 63 mm from a single-track MLS point cloud with a high reliability. The paper further discusses the strength and the challenges of individual particle-based approach for sedimentary measurement.

Terrestrial laser scanning (TLS) enables the efficient production of high-density colored 3D point clouds of real-world environments. An increasing number of applications from visual and automated interpretation to photorealistic 3D visualizations and experiences rely on accurate and reliable color information. However, insufficient attention has been put into evaluating the colorization quality of the 3D point clouds produced applying TLS. We have developed a method for the evaluation of the point cloud colorization quality of TLS systems with integrated imaging sensors. Our method assesses the capability of several tested systems to reproduce colors and details of a scene by measuring objective image quality metrics from 2D images that were rendered from 3D scanned test charts. The results suggest that the detected problems related to color reproduction (i.e., measured differences in color, white balance, and exposure) could be mitigated in data processing while the issues related to detail reproduction (i.e., measured sharpness and noise) are less in the control of a scanner user. Despite being commendable 3D measuring instruments, improving the colorization tools and workflows, and automated image processing pipelines would potentially increase not only the quality and production efficiency but also the applicability of colored 3D point clouds.

A novel approach to evaluating night-time road and street environment lighting conditions through 3D point clouds is presented. The combination of luminance imaging and 3D point cloud acquired with a terrestrial laser scanner was used for analyzing 3D luminance on the road surface. A calculation of the luminance (cd/m²) was based on the RGB output values of a Nikon D800E digital still camera. The camera was calibrated with a reference luminance source. The relative orientation between the luminance images and intensity image of the 3D point cloud was solved in order to integrate the data sets into the same coordinate system. As a result, the 3D model of road environment luminance is illustrated and the ability to exploit the method for evaluating the luminance distribution on the road surface is presented. Furthermore, the limitations and future prospects of the methodology are addressed. The method provides promising results for studying road lighting conditions in future lighting optimizations. The paper presents the methodology and its experimental application on a road section which consists of five luminaires installed on one side of a two-lane road in Otaniemi, Espoo, Finland.