Einführung in das CaRo-Projekt: Geometrie- und Texturerfassung von 3D-Objekten mit robotergeführter Videokamera

Mit dem CaRo-Projekt (CaRo = Camera Roboter) wird an der Universität Karlsruhe ein neuartiger Ansatz verfolgt, um das Problem der Erfassung von Form- und Oberflächendaten für 3D-Objekte zu lösen. Bei CaRo führt ein Roboterarm eine Kamera und richtet sie gemäß der jeweiligen Erfassungsstrategie aus verschiedenen Richtungen auf das zu digitalisie- rende Objekt, z. B. ein Werkstück des Maschinenbaus. Mit Bildanalyse- verfahren können nun die Koordinaten von Oberflächenpunkten des Werkstücks bestimmt werden. Diese Vorgehensweise hat mehrere Vorteile. Die Kameraführung erfolgt adaptiv und wird von der Analysesoftware vorgegeben. Zwischen Globalsichten und Detailvergrößerungen kann ständig gewechselt werden. Die Oberfläche des Werkstücks kann in hochauflösenden Farbbildern repräsentiert werden, so daß Textur und Farbe zusammen mit den Geometriedaten geliefert werden können. Die Flexibilität des Ansatzes wird dadurch deutlich, daß sich mit ihm z.B. unterschiedlichste Anforderungen erfüllen lassen. So besteht die Möglichkeit, auch Bücher mit nicht planliegenden Seiten vollautomatisch zu digitalisieren, wobei nicht die 3D-Geometrie, sondern die Textrepräsentation im Vordergrund steht. Weiterhin können mit dem CaRo-Ansatz dreidimensionale Daten erzeugt werden, wie sie z. B. beim Reverse Engineering und in der Werbe- und Filmindustrie (virtuelle Welten, Computeranimationen in der Werbung, Trickfilm) benötigt werden. Die bisher bekanntgewordenen Digitalisiergeräte können die geforderte Breite und Flexibilitdt der Objektdigitalisierung nicht leisten. Es besteht diebegründete Hoffnung, daß der CaRo-Ansatz, also das bewegte Kameraauge, zu einer Standard-Eingabetechnik für die graphische Datenverarbeitung ausgebaut werden kann

3D object reconstruction and representation using neural networks

3D object reconstruction is frequent used in various fields such as product design, engineering, medical and artistic applications. Numerous reconstruction techniques and software were introduced and developed. However, the purpose of this paper is to fully integrate an adaptive artificial neural network (ANN) based method in reconstructing and representing 3D objects. This study explores the ability of neural networks in learning through experience when reconstructing an object by estimating it’s z-coordinate. Neural networks ’ capability in representing most classes of 3D objects used in computer graphics is also proven. Simple affined transformation is applied on different objects using this approach and compared with the real objects. The results show that neural network is a promising approach for reconstruction and representation of 3D objects

Automatic Reconstruction of 3D Objects Using a Mobile Monoscopic Camera

A method for the automatic reconstruction of 3D objects from multiple camera views for 3D multimedia applications is presented. Conventional 3D reconstruction techniques use equipment that restrict the flexibility of the user. In order to increase this flexibility, the presented method is characterized by a simple measurement environment, that consists of a new calibration pattern placed below the object allowing object and pattern acquisition simultaneously. This ensures, that each view can be calibrated individually. From these obtained calibrated camera views, a textured 3D wireframe model is estimated using a shape--from--silhouette approach and texture mapping of the original camera views. Experiments with this system have confirmed a significant gain of flexibility for the user and a drastic reduction of costs for technical equipment while ensuring comparable model quality as conventional reconstruction techniques at the same time. 1 Introduction Natural looking 3D models of re..

Numérisation 3D de visages par une approche de super-résolution spatio-temporelle non-rigide

La mesure de la forme 3D du visage est une problématique qui attire de plus en plus de chercheurs et qui trouve son application dans des domaines divers tels que la biométrie, l animation et la chirurgie faciale. Les solutions actuelles sont souvent basées sur des systèmes projecteur/caméra et utilisent de la lumière structurée pour compenser l insuffisance de la texture faciale. L information 3D est ensuite calculée en décodant la distorsion des patrons projetés sur le visage. Une des techniques les plus utilisées de la lumière structurée est la codification sinusoïdale par décalage de phase qui permet une numérisation 3D de résolution pixélique. Cette technique exige une étape de déroulement de phase, sensible à l éclairage ambiant surtout quand le nombre de patrons projetés est limité. En plus, la projection de plusieurs patrons impacte le délai de numérisation et peut générer des artefacts surtout pour la capture d un visage en mouvement. Une alternative aux approches projecteur-caméra consiste à estimer l information 3D par appariement stéréo suivi par une triangulation optique. Cependant, le modèle calculé par cette technique est généralement non-dense et manque de précision. Des travaux récents proposent la super-résolution pour densifier et débruiter les images de profondeur. La super-résolution a été particulièrement proposée pour les caméras 3D TOF (Time-Of-Flight) qui fournissent des scans 3D très bruités. Ce travail de thèse propose une solution de numérisation 3D à faible coût avec un schéma de super-résolution spatio-temporelle. Elle utilise un système multi-caméra étalonné assisté par une source de projection non-étalonnée. Elle est particulièrement adaptée à la reconstruction 3D de visages, i.e. rapide et mobile. La solution proposée est une approche hybride qui associe la stéréovision et la codification sinusoïdale par décalage de phase, et qui non seulement profite de leurs avantages mais qui surmonte leurs faiblesses. Le schéma de la super-résolution proposé permet de corriger l information 3D, de compléter la vue scannée du visage en traitant son aspect déformable.3D face measurement is increasingly demanded for many applications such as bio-metrics, animation and facial surgery. Current solutions often employ a structured light camera/projector device to overcome the relatively uniform appearance of skin. Depth in-formation is recovered by decoding patterns of the projected structured light. One of the most widely used structured-light coding is sinusoidal phase shifting which allows a 3Ddense resolution. Current solutions mostly utilize more than three phase-shifted sinusoidal patterns to recover the depth information, thus impacting the acquisition delay. They further require projector-camera calibration whose accuracy is crucial for phase to depth estimation step. Also, they need an unwrapping stage which is sensitive to ambient light, especially when the number of patterns decreases. An alternative to projector-camera systems consists of recovering depth information by stereovision using a multi-camera system. A stereo matching step finds correspondence between stereo images and the 3D information is obtained by optical triangulation. However, the model computed in this way generally is quite sparse. To up sample and denoise depth images, researchers looked into super-resolution techniques. Super-resolution was especially proposed for time-of-flight cameras which have very low data quality and a very high random noise. This thesis proposes a3D acquisition solution with a 3D space-time non-rigid super-resolution capability, using a calibrated multi-camera system coupled with a non calibrated projector device, which is particularly suited to 3D face scanning, i.e. rapid and easily movable. The proposed solution is a hybrid stereovision and phase-shifting approach, using two shifted patterns and a texture image, which not only takes advantage of the assets of stereovision and structured light but also overcomes their weaknesses. The super-resolution scheme involves a 3D non-rigid registration for 3D artifacts correction in the presence of small non-rigid deformations as facial expressions.LYON-Ecole Centrale (690812301) / SudocSudocFranceF

A graph-theory-based C-space path planner for mobile robotic manipulators in close-proximity environments

In this thesis a novel guidance method for a 3-degree-of-freedom robotic manipulator arm in 3 dimensions for Improvised Explosive Device (IED) disposal has been developed. The work carried out in this thesis combines existing methods to develop a technique that delivers advantages taken from several other guidance techniques. These features are necessary for the IED disposal application. The work carried out in this thesis includes kinematic and dynamic modelling of robotic manipulators, T-space to C-space conversion, and path generation using Graph Theory to produce a guidance technique which can plan a safe path through a complex unknown environment. The method improves upon advantages given by other techniques in that it produces a suitable path in 3-dimensions in close-proximity environments in real time with no a priori knowledge of the environment, a necessary precursor to the application of this technique to IED disposal missions. To solve the problem of path planning, the thesis derives the kinematics and dynamics of a robotic arm in order to convert the Euclidean coordinates of measured environment data into C-space. Each dimension in C-space is one control input of the arm. The Euclidean start and end locations of the manipulator end effector are translated into C-space. A three-dimensional path is generated between them using Dijkstra’s Algorithm. The technique allows for a single path to be generated to guide the entire arm through the environment, rather than multiple paths to guide each component through the environment. The robotic arm parameters are modelled as a quasi-linear parameter varying system. As such it requires gain scheduling control, thus allowing compensation of the non-linearities in the system. A Genetic Algorithm is applied to tune a set of PID controllers for the dynamic model of the manipulator arm so that the generated path can then be followed using a conventional path-following algorithm. The technique proposed in this thesis is validated using numerical simulations in order to determine its advantages and limitations