3D precision measurements of meter sized surfaces using low cost illumination and camera techniques

Abstract

Using dedicated stereo camera systems and structured light is a well-known method for measuring the 3D shape of large surfaces. However the problem is not trivial when high accuracy, in the range of few tens of microns, is needed. Many error sources need to be handled carefully in order to obtain high quality results. In this study, we present a measurement method based on low-cost camera and illumination solutions combined with high-precision image analysis and a new approach in camera calibration and 3D reconstruction. The setup consists of two ordinary digital cameras and a Gobo projector as a structured light source. A matrix of dots is projected onto the target area. The two cameras capture the images of the projected pattern on the object. The images are processed by advanced subpixel resolution algorithms prior to the application of the 3D reconstruction technique. The strength of the method lays in a different approach for calibration, 3D reconstruction, and high-precision image analysis algorithms. Using a 10 mm pitch pattern of the light dots, the method is capable of reconstructing the 3D shape of surfaces. The precision (1σ repeatability) in the measurements is <10 µm over a volume of 60 × 50 × 10 cm3 at a hardware cost of ~2% of available advanced measurement techniques. The expanded uncertainty (95% confidence level) is estimated to be 83 µm, with the largest uncertainty contribution coming from the absolute length of the metal ruler used as reference.

TraceableX,Yself-calibration at single nm level of an optical microscope used for coherence scanning interferometry

“…the images of the artefact dots as output. The subpixel image processing tool is described in detail in [13]. The COG data of all images, translated and rotated, are then fed into the selfcalibration module where a recursive algorithm performs the calculations and ends up with an instrument correction function and the metric coordinates of the artefact dots, by using the subpixel analyzed images, obtained with the same instrument of a traceable 1D ruler scale.…”

“…When the location of the gradient is roughly known, these coordinates are used as input to an advanced sub-pixel algorithm. This algorithm uses a lot more information in the neighborhood of the edge to make a more precise estimation of the edge location [13,18]. The last task is then to fit a circle (or an ellipse) to the sub-pixel coordinates found by the latter algorithm.…”

3D Face Geometry Optimization Using Artificial Intelligence and Computer Graphics

“…In the field of artificial intelligence, face recognition technology is also gradually developing to the field of 3D face application. e real 3D face image data are collected and reconstructed through the high-precision 3D face camera [1], and a 3D face model similar to the real one is obtained. However, the collected three-dimensional information often lacks measurement data, resulting in the lack of holes in the reconstructed three-dimensional model, which cannot be applied in practice.…”

A Benchmark of Popular Indoor 3D Reconstruction Technologies: Comparison of ARCore and RTAB-Map

“…When two cameras are placed next to each other at a known distance, and the corresponding images' differences are used to calculate depth data, we are talking about a stereoscopic camera. Since this approach is feature-based, it requires the object of interest to have a distinguishable texture or feature such as a known dimension [4]. A stereoscopic camera is not capable of detecting homogeneous surfaces on its own.…”