In the current modern era of information and technology, emerging remote advancements have been widely established for detailed virtual inspections and assessments of infrastructure assets, especially bridges. These technologies are capable of creating an accurate digital representation of the existing assets, commonly known as the digital twins. Digital twins are suitable alternatives to in-person and on-site based assessments that can provide safer, cheaper, more reliable, and less distributive bridge inspections. In the case of bridge monitoring, Unmanned Aerial Vehicle (UAV) photogrammetry and Terrestrial Laser Scanning (TLS) are among the most common advanced technologies that hold the potential to provide qualitative digital models; however, the research is still lacking a reliable methodology to evaluate the generated point clouds in terms of quality and geometric accuracy for a bridge size case study. Therefore, this paper aims to provide a comprehensive methodology along with a thorough bridge case study to evaluate two digital point clouds developed from an existing Australian heritage bridge via both UAV-based photogrammetry and TLS. In this regard, a range of proposed approaches were employed to compare point clouds in terms of points’ distribution, level of outlier noise, data completeness, surface deviation, and geometric accuracy. The comparative results of this case study not only proved the capability and applicability of the proposed methodology and approaches in evaluating these two voluminous point clouds, but they also exhibited a higher level of point density and more acceptable agreements with as-is measurements in TLS-based point clouds subjected to the implementation of a precise data capture and a 3D reconstruction model.

This paper presents a critical analysis of image-based 3D reconstruction using neural radiance fields (NeRFs), with a focus on quantitative comparisons with respect to traditional photogrammetry. The aim is, therefore, to objectively evaluate the strengths and weaknesses of NeRFs and provide insights into their applicability to different real-life scenarios, from small objects to heritage and industrial scenes. After a comprehensive overview of photogrammetry and NeRF methods, highlighting their respective advantages and disadvantages, various NeRF methods are compared using diverse objects with varying sizes and surface characteristics, including texture-less, metallic, translucent, and transparent surfaces. We evaluated the quality of the resulting 3D reconstructions using multiple criteria, such as noise level, geometric accuracy, and the number of required images (i.e., image baselines). The results show that NeRFs exhibit superior performance over photogrammetry in terms of non-collaborative objects with texture-less, reflective, and refractive surfaces. Conversely, photogrammetry outperforms NeRFs in cases where the object’s surface possesses cooperative texture. Such complementarity should be further exploited in future works.

Image-based 3D reconstruction has been employed in industrial metrology for micro-measurements and quality control purposes. However, generating a highly-detailed and reliable 3D reconstruction of non-collaborative surfaces is still an open issue. In this paper, a method for generating an accurate 3D reconstruction of non-collaborative surfaces through a combination of photogrammetry and photometric stereo is presented. On one side, the geometric information derived with photogrammetry is used in areas where its 3D measurements are reliable. On the other hand, the high spatial resolution capability of photometric stereo is exploited to acquire a finely detailed topography of the surface. Finally, three different approaches are proposed to fuse both geometric information and high frequency details. The proposed method is tested on six different non-collaborative objects with different surface characteristics. To evaluate the accuracy of the proposed method, a comprehensive cloud-to-cloud comparison between reference data and 3D points derived from the proposed fusion methods is provided. The experiments demonstrated that, despite correcting global deformation up to an average RMSE of less than 0.1 mm, the proposed method recovers the surface topography at the same high resolution as the photometric stereo.

Three‐dimensional (3D) measurement of non‐collaborative surfaces is still an open research topic. This paper investigates and quantifies for the first time the effect of light directionality and fusion of multiple images as a method to improve the quality of photogrammetric 3D reconstruction. For this aim, an image acquisition system that employs multiple light sources was developed to highlight the roughness and microstructures of the object under investigation. Images were captured at various grazing angles to highlight the local surface roughness and microstructures. Individual point clouds, created using images taken at different grazing angles, were produced using dense image‐matching techniques. These point clouds were then compared against different 3D photogrammetric reconstructions obtained from a pre‐processing of the acquired images based on diffuse lighting, median and average images. Experiments showed that exploiting light directionality significantly improves image‐matching quality. Furthermore, depending on the light direction, the root mean square (RMS) error of the 3D surfaces obtained using the proposed system were up to 50% less than those created by traditional diffuse lighting.

Sustainable and livable cities and non-motorized transportation modes are being increasingly promoted around the world. A considerable number of studies have investigated various factors associated with the severity of pedestrian crashes. However, little attention has been paid to the particular impacts of lighting conditions. This paper aims to differentiate between different lighting conditions based on solar altitude by developing separate Generalized Ordered Probit (GOP) models for the four distinct lighting conditions (i.e. daylight, twilight, dark, and lighted) analysing pedestrian injury severity at urban intersections. The models are tested using a set of log-likelihood ratio tests, which show there are key differences in the significance of the estimates, magnitude, and even the direction of the effects of explanatory variables, in which such differences may not be revealed by calibrating one aggregate model. Notably, built environments, age, gender of both pedestrian and driver, and speed limits were found to have different impacts on the severity of the crash.

Image-based 3D reconstruction has been successfully employed for micro-measurements and industrial quality control purposes. However, obtaining a highly-detailed and reliable 3D reconstruction and inspection of non-collaborative surfaces is still an open issue. Photometric stereo (PS) offers the high spatial frequencies of the surface, but the low frequency is erroneous due to the mathematical model's assumptions and simplifications on how light interacts with the object surface. Photogrammetry, on the other hand, gives precise low-frequency information but fails to utilize high frequencies. As a result, in this research, we present a fusion strategy in Fourier domain to replace the low spatial frequencies of PS with the corresponding photogrammetric frequencies in order to have correct low frequencies while maintaining high frequencies from PS. The proposed method was tested on three different objects. Different cloud-to-cloud comparisons were provided between reference data and the 3D points derived from the proposed method to evaluate high and low frequency information. The obtained 3D findings demonstrated how the proposed methodology generates a high-detail 3D reconstruction of the surface topography (below 20 µm) while maintaining low-frequency information (0.09 µm on average for three different testing objects) by fusing photogrammetric and PS depth data with the proposed FFT-based method.