A System for In-Line 3D Inspection without Hidden Surfaces

Abstract

This work presents a 3D scanner able to reconstruct a complete object without occlusions, including its surface appearance. The technique presents a number of differences in relation to current scanners: it does not require mechanical handling like robot arms or spinning plates, it is free of occlusions since the scanned part is not resting on any surface and, unlike stereo-based methods, the object does not need to have visual singularities on its surface. This system, among other applications, allows its integration in production lines that require the inspection of a large volume of parts or products, especially if there is an important variability of the objects to be inspected, since there is no mechanical manipulation. The scanner consists of a variable number of industrial quality cameras conveniently distributed so that they can capture all the surfaces of the object without any blind spot. The object is dropped through the common visual field of all the cameras, so no surface or tool occludes the views that are captured simultaneously when the part is in the center of the visible volume. A carving procedure that uses the silhouettes segmented from each image gives rise to a volumetric representation and, by means of isosurface generation techniques, to a 3D model. These techniques have certain limitations on the reconstruction of object regions with particular geometric configurations. Estimating the inherent maximum error in each area is important to bound the precision of the reconstruction. A number of experiments are presented reporting the differences between ideal and reconstructed objects in the system.

An Overview on Image-Based and Scanner-Based 3D Modeling Technologies

“…Various classifications of 3D scanners have been proposed in the scientific literature [ 45 , 46 , 47 , 48 , 49 ]. Figure 3 shows a classification of 3D scanners, which is adopted in the context of this article.…”

Calibration Procedure of a Multi-Camera System: Process Uncertainty Budget

“…However, the 3D evaluation is not reliable as it does not have a ground truth to verify. Perez-Cortes et al [ 10 ] also follows a similar strategy, but instead of using a pointer, a sphere is used in 16 positions, and it is solved through a set of camera projections of the epipolar lines. Robson et al [ 11 , 12 ] carried out a photogrammetric calibration procedure (bundle adjustment) through the Manhattan Vision Metrology System (VMS) pattern to solve intrinsic parameters, extrinsic parameters, and 3D coordinates in one go.…”

“…Perez et al [ 15 ] calibrate it using two spheres and Zhang et al [ 16 ] follow planar pattern methodologies to calibrate both intrinsic and extrinsic parameters. Planar pattern calibration techniques where chess boards [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ] or other types of 3D patterns [ 10 ] are used have limitations in terms of high-range scenarios as very large patterns would be requested and all cameras can see the same work areas. In this sense, contributions such as Xing et al’s.…”

Probabilistic Evaluation of 3D Surfaces Using Statistical Shape Models (SSM)

“…Other systems make use of robotic arms to move the object in front of a sensor such as Fei et al [ 4 ] or Brosed et al [ 5 ]. It is also possible to obtain the 3D reconstruction from multiple views as Perez-Cortes et al [ 6 ] show. In Bi et al [ 7 ] a survey of non-contact 3D scanners with a classification of their technologies from a manufacturing perspective is presented.…”

“…Although the results of this work are generalizable, the experiments presented use a 3D reconstruction technique from multiple silhouettes as explained in Perez-Cortes et al [ 6 ] and 3D models acquired with a ZG3D patented device [ 11 ] as the one shown in Figure 1 .…”