Mikko Maksimainen

We aim to present a method to measure 3D luminance point clouds by applying the integrated high dynamic range (HDR) panoramic camera system of a terrestrial laser scanning (TLS) instrument for performing luminance measurements simultaneously with laser scanning. We present the luminance calibration of a laser scanner and assess the accuracy, color measurement properties, and dynamic range of luminance measurement achieved in the laboratory environment. In addition, we demonstrate the 3D luminance measuring process through a case study with a luminance-calibrated laser scanner. The presented method can be utilized directly as the luminance data source. A terrestrial laser scanner can be prepared, characterized, and calibrated to apply it to the simultaneous measurement of both geometry and luminance. We discuss the state and limitations of contemporary TLS technology for luminance measuring.

Computer-aided 3D modelling is the standard design method in the architecture, engineering, construction, owner, operator (AECOO) industry and in lighting design. Applying a photogrammetric process, a sequence of images is used to reconstruct the geometry of a space or an object in a 3D model. Likewise, a calibrated digital camera is utilized to measure the surface luminance values of an environment or an object. We propose a workflow in which the geometry and the luminance values are measured simultaneously by combining these two measurement methods. The pipeline has been assessed and validated through the application to a case study, the Aalto Hall (Aalto University, Espoo, Finland), in order to understand its potential.

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.

Roadside vegetation can affect the performance of installed road lighting. We demonstrate a workflow in which a car-mounted measurement system is used to assess the light-obstructing effect of roadside vegetation. The mobile mapping system (MMS) includes a panoramic camera system, laser scanner, inertial measurement unit, and satellite positioning system. The workflow and the measurement system were applied to a road section of Munkkiniemenranta, Helsinki, Finland, in 2015 and 2019. The relative luminance distribution on a road surface and the obstructing vegetation were measured before and after roadside vegetation pruning applying a luminance-calibrated mobile mapping system. The difference between the two measurements is presented, and the opportunities provided by the mobile 3D luminance measurement system are discussed.

The Internet has become a major dissemination and sharing platform for 3D content. The utilization of 3D measurement methods can drastically increase the production efficiency of 3D content in an increasing number of use cases where 3D documentation of real-life objects or environments is required. We demonstrated a developed, highly automated and integrated content creation process of providing reality-based photorealistic 3D models for the web. Close-range photogrammetry, terrestrial laser scanning (TLS) and their combination are compared using available state-of-the-art tools in a real-life project setting with real-life limitations. Integrating photogrammetry and TLS is a good compromise for both geometric and texture quality. Compared to approaches using only photogrammetry or TLS, it is slower and more resource-heavy but combines complementary advantages of each method, such as direct scale determination from TLS or superior image quality typically used in photogrammetry. The integration is not only beneficial, but clearly productionally possible using available state-of-the-art tools that have become increasingly available also for non-expert users. Despite the high degree of automation, some manual editing steps are still required in practice to achieve satisfactory results in terms of adequate visual quality. This is mainly due to the current limitations of WebGL technology.

This study describes a method of measuring the pixelwise S/P ratio using a calibrated digital camera and applying these S/P ratios to calculate mesopic luminance values. An example of using the developed method is presented. Further improvements for the method are discussed. Graphical Abstract

In mesopic photometry, adaptation luminance is needed to derive the mesopic luminances for the measurement field. The average luminance of the visual adaptation field is considered as the adaptation luminance. The visual adaptation field has yet to be defined in terms of the size, shape, or location within the visual field. A study in three road lighting situations was conducted, in order to determine the feasibility of using the road surface as the adaptation field compared to circular or elliptical adaptation fields. Currently, the road surface is used as the measurement field for calculating road lighting. Using the road surface as the adaptation field resulted in 76–113%, higher average luminance than obtained using circular or elliptical adaptation fields when the road was bordered by a park. High-luminance sources outside of the visual adaptation field cause veiling luminance. Veiling luminance increases the adaptation state, but not the luminance within the measurement field. The bias veiling luminance can cause on mesopic luminance calculations was estimated to be less than 2%. The estimated bias can be considered trivial in practical road lighting measurements.

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.