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.

Several calibration algorithms use spheres as calibration tokens because of the simplicity and uniform shape that a sphere presents across multiple views, along with the simplicity of its construction. Among the alternatives are the use of complex 3D tokens with reference marks, usually complex to build and analyze with the required accuracy; or the search of common features in scene images, with this task being of high complexity too due to perspective changes. Some of the algorithms using spheres rely on the estimation of the sphere center projection obtained from the camera images to proceed. The computation of these projection points from the sphere silhouette on the images is not straightforward because it does not match exactly the silhouette centroid. Thus, several methods have been developed to cope with this calculation. In this work, a simple and fast numerical method adapted to precisely compute the sphere center projection for these algorithms is presented. The benefits over other similar existing methods are its ease of implementation and that it presents less sensibility to segmentation issues. Other possible applications of the proposed method are presented too.