Quantifying the Influence of Surface Texture and Shape on Structure from Motion 3D Reconstructions

In general, optical methods for geometrical measurements are influenced by the surface properties of the examined object. In Structure from Motion (SfM), local variations in surface color or topography are necessary for detecting feature points for point-cloud triangulation. Thus, the level of contrast or texture is important for an accurate reconstruction. However, quantitative studies of the influence of surface texture on geometrical reconstruction are largely missing. This study tries to remedy that by investigating the influence of object texture levels on reconstruction accuracy using a set of reference artifacts. The artifacts are designed with well-defined surface geometries, and quantitative metrics are introduced to evaluate the lateral resolution, vertical geometric variation, and spatial–frequency information of the reconstructions. The influence of texture level is compared to variations in capturing range. For the SfM measurements, the ContextCapture software solution and a 50 Mpx DSLR camera are used. The findings are compared to results using calibrated optical microscopes. The results show that the proposed pipeline can be used for investigating the influence of texture on SfM reconstructions. The introduced metrics allow for a quantitative comparison of the reconstructions at varying texture levels and ranges. Both range and texture level are seen to affect the reconstructed geometries although in different ways. While an increase in range at a fixed focal length reduces the spatial resolution, an insufficient texture level causes an increased noise level and may introduce errors in the reconstruction. The artifacts are designed to be easily replicable, and by providing a step-by-step procedure of our testing and comparison methodology, we hope that other researchers will make use of the proposed testing pipeline

Udviklingslaboratorier som metode til kompetenceudvikling i teknologiforståelse: Erfaringer med TEKFAG-modellen

Artiklen beskriver forfatternes egne erfaringer med udviklingslaboratorier som metode til kompetenceudvikling af undervisere på læreruddannelsen i teknologiforståelse som delfaglighed i undervisningsfaget dansk og grundfaget pædagogik og lærerfaglighed (PL). Indledningsvis redegøres for teori om udviklingslaboratorier og teknologiforståelse, hvorefter forfatterne redegør for egne ‘didaktiske refleksioner’ over to af de afholdte udviklingslaboratorier. Afslutningsvis diskuteres sigtelinjer for det gode udviklingslaboratorie i en kompetenceudviklingssammenhæng. Artiklens to refleksioner fremhæver især, hvordan udviklingslaboratoriet kan åbne udviklingsrum i veletablerede fag og fagligheder, hvordan de afhænger af deltagernes kompetencer og motivation til at foretage konkrete eksperimenter og ikke mindst hvordan laboratorier kan fremme fagmøder og meningsskabelse mellem de mange fag og fagområder, der er impliceret i teknologiforståelse i dansk og PL.The article describes the intentions behind the TEKFAG-model for competence development in ‘technology comprehension’ as a new subject area on digital technologies nested within other subject areas such as Danish and Pedagogy and Teachers Professionalism. The model is focused on the competence development of teacher educators and designed to manage the specific challenges associated with a subject area (technology comprehension), that is not only nested within other subject areas, but also in itself under development. The TEKFAG-model is based on a method called ‘development laboratories’ and the article investigates the intentions and characteristics of these laboratories in both theory and practice. It is discussed how development laboratories may help develop sustainable competences in technology comprehension as a new and evolving subject area

High-Resolution Structure-from-Motion for Quantitative Measurement of Leading-Edge Roughness

Over time, erosion of the leading edge of wind turbine blades increases the leading-edge roughness (LER). This may reduce the aerodynamic performance of the blade and hence the annual energy production of the wind turbine. As early detection is key for cost-effective maintenance, inspection methods are needed to quantify the LER of the blade. The aim of this proof-of-principle study is to determine whether high-resolution Structure-from-Motion (SfM) has the sufficient resolution and accuracy for quantitative inspection of LER. SfM provides 3D reconstruction of an object geometry using overlapping images of the object acquired with an RGB camera. Using information of the camera positions and orientations, absolute scale of the reconstruction can be achieved. Combined with a UAV platform, SfM has the potential for remote blade inspections with a reduced downtime. The tip of a decommissioned blade with an artificially enhanced erosion was used for the measurements. For validation, replica molding was used to transfer areas-of-interest to the lab for reference measurements using confocal microscopy. The SfM reconstruction resulted in a spatial resolution of 1 mm as well as a sub-mm accuracy in both the RMS surface roughness and the size of topographic features. In conclusion, high-resolution SfM demonstrated a successful quantitative reconstruction of LER

Quantifying the Influence of Surface Texture and Shape on Structure from Motion 3D Reconstructions

In general, optical methods for geometrical measurements are influenced by the surface properties of the examined object. In Structure from Motion (SfM), local variations in surface color or topography are necessary for detecting feature points for point-cloud triangulation. Thus, the level of contrast or texture is important for an accurate reconstruction. However, quantitative studies of the influence of surface texture on geometrical reconstruction are largely missing. This study tries to remedy that by investigating the influence of object texture levels on reconstruction accuracy using a set of reference artifacts. The artifacts are designed with well-defined surface geometries, and quantitative metrics are introduced to evaluate the lateral resolution, vertical geometric variation, and spatial–frequency information of the reconstructions. The influence of texture level is compared to variations in capturing range. For the SfM measurements, the ContextCapture software solution and a 50 Mpx DSLR camera are used. The findings are compared to results using calibrated optical microscopes. The results show that the proposed pipeline can be used for investigating the influence of texture on SfM reconstructions. The introduced metrics allow for a quantitative comparison of the reconstructions at varying texture levels and ranges. Both range and texture level are seen to affect the reconstructed geometries although in different ways. While an increase in range at a fixed focal length reduces the spatial resolution, an insufficient texture level causes an increased noise level and may introduce errors in the reconstruction. The artifacts are designed to be easily replicable, and by providing a step-by-step procedure of our testing and comparison methodology, we hope that other researchers will make use of the proposed testing pipeline