Search

3D object alignment is essential in multiple fields. For instance, to allow precise measurements in metrology, to perform surface/volumetric checks or quality control in industrial inspection, to align partial captures of a 3D object during object scanning, to simplify object recognition or classification in pattern recognition, accuracy and speed, being opposed, are desirable features of those algorithms. Nevertheless, they can be more or less critical depending on the application area. In the present work, we propose a methodology to improve the alignment of 3D objects reconstructed using shape-from-silhouette techniques. This reconstruction technique produces objects with small synthetic bulges, making them more difficult to align accurately. On the one hand, prealignment and branch-and-bound techniques are used to improve the convergence and speed of the alignment algorithms. On the other hand, a method to obtain a precise alignment even in the presence of bulges is presented. Finally, a refinement of the shape-from-silhouettes technique is shown. This technique uses multiple captures to refine object reconstruction and reduce or eliminate, among other improvements, synthetic bulges.

Pose estimation assesses the 6D pose of one or many objects in a scene. Considerable attention has been dedicated to the advancement of pose estimation algorithms capable of identifying the orientation of multiple objects within a scene in cases where partial occlusion occurs. However, only a few works focus on developing a parallelizable hypotheses-based estimator that naturally handles object symmetries. These algorithms should also tackle some issues: meaningless perspectives, objects with multiple uncertain local poses but a single global correct pose, and multiple correct poses. This paper proposes a novel probabilistic algorithm for pose estimation that addresses these issues. This probabilistic algorithm combines the information from multiple cameras to achieve a unique prediction that assembles global object information. The algorithm is tested over synthetic objects that simulate these issues. It achieves a rotation error below 1.5°, and a translation error of 1.5 pixels in the datasets used. Those results suggest that the algorithm can handle the mentioned issues up to a certain accuracy. Additionally, the method is compared against a state-of-the-art methodology of the LineMOD dataset. This comparison shows that our algorithm can compete against state-of-the-art algorithms in terms of accuracy.

3D Geometric quality inspection involves assessing and comparing a reconstructed object to a predefined reference model or design that defines its expected volume. Achieving precise 3D object geometry reconstruction from multiple views can be challenging. In this research, we propose a camera-coherent point selection method to measure differences with the reference. The result is a point cloud extracted from the reconstruction that represents the best-case scenario, ensuring that any deviations from the reference are represented as seen from the cameras. This algorithm has been tested in both simulated and real conditions, reducing reconstruction errors by up to one fifth compared to traditional 3D reconstruction methodologies. Furthermore, this strategy assures that any existing difference with its reference really exists and it is a best-case scenario. It offers a fast and robust pipeline for comprehensive 3D geometric quality assurance, contributing significantly to advancements in the field of 3D object inspection.

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.

Inspecting a 3D object which shape has elastic manufacturing tolerances in order to find defects is a challenging and time-consuming task. This task usually involves humans, either in the specification stage followed by some automatic measurements, or in other points along the process. Even when a detailed inspection is performed, the measurements are limited to a few dimensions instead of a complete examination of the object. In this work, a probabilistic method to evaluate 3D surfaces is presented. This algorithm relies on a training stage to learn the shape of the object building a statistical shape model. Making use of this model, any inspected object can be evaluated obtaining a probability that the whole object or any of its dimensions are compatible with the model, thus allowing to easily find defective objects. Results in simulated and real environments are presented and compared to two different alternatives.

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.