Fast Characterization of Moving Samples with Nano-Textured Surfaces

We characterize nano-textured surfaces by optical diffraction techniques using an adapted commercial light microscope with two detectors, a CCD camera and a spectrometer. The acquisition and analyzing time for the topological parameters height, width, and sidewall angle is only a few milliseconds of a grating. We demonstrate that the microscope has a resolution in the nanometer range, also in an environment with many vibrations, such as a machine floor. Furthermore, we demonstrate an easy method to find the area of interest with the integrated CCD camera.Comment: 19 pages, 4 figure

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