Automatic co-registration of aerial imagery and untextured model data utilizing average shading gradients

The comparison of current image data with existing 3D model data of a scene provides an efficient method to keep models up to date. In order to transfer information between 2D and 3D data, a preliminary co-registration is necessary. In this paper, we present a concept to automatically co-register aerial imagery and untextured 3D model data. To refine a given initial camera pose, our algorithm computes dense correspondence fields using SIFT flow between gradient representations of the model and camera image, from which 2D–3D correspondences are obtained. These correspondences are then used in an iterative optimization scheme to refine the initial camera pose by minimizing the reprojection error. Since it is assumed that the model does not contain texture information, our algorithm is built up on an existing method based on Average Shading Gradients (ASG) to generate gradient images based on raw geometry information only. We apply our algorithm for the co-registering of aerial photographs to an untextured, noisy mesh model. We have investigated different magnitudes of input error and show that the proposed approach can reduce the final reprojection error to a minimum of 1.27 ± 0.54 pixels, which is less than 10% of its initial value. Furthermore, our evaluation shows that our approach outperforms the accuracy of a standard Iterative Closest Point (ICP) implementation

Live User-Guided Intrinsic Video for Static Scenes

We present a novel real-time approach for user-guided intrinsic decomposition of static scenes captured by an RGB-D sensor. In the first step, we acquire a three-dimensional representation of the scene using a dense volumetric reconstruction framework. The obtained reconstruction serves as a proxy to densely fuse reflectance estimates and to store user-provided constraints in three-dimensional space. User constraints, in the form of constant shading and reflectance strokes, can be placed directly on the real-world geometry using an intuitive touch-based interaction metaphor, or using interactive mouse strokes. Fusing the decomposition results and constraints in three-dimensional space allows for robust propagation of this information to novel views by re-projection.We leverage this information to improve on the decomposition quality of existing intrinsic video decomposition techniques by further constraining the ill-posed decomposition problem. In addition to improved decomposition quality, we show a variety of live augmented reality applications such as recoloring of objects, relighting of scenes and editing of material appearance

Modelling appearance and geometry from images

Acquisition of realistic and relightable 3D models of large outdoor structures, such as buildings, requires the modelling of detailed geometry and visual appearance. Recovering these material characteristics can be very time consuming and needs specially dedicated equipment. Alternatively, surface detail can be conveyed by textures recovered from images, whose appearance is only valid under the originally photographed viewing and lighting conditions. Methods to easily capture locally detailed geometry, such as cracks in stone walls, and visual appearance require control of lighting conditions, which are usually restricted to small portions of surfaces captured at close range.This thesis investigates the acquisition of high-quality models from images, using simple photographic equipment and modest user intervention. The main focus of this investigation is on approximating detailed local depth information and visual appearance, obtained using a new image-based approach, and combining this with gross-scale 3D geometry. This is achieved by capturing these surface characteristics in small accessible regions and transferring them to the complete façade. This approach yields high-quality models, imparting the illusion of measured reflectance. In this thesis, we first present two novel algorithms for surface detail and visual appearance transfer, where these material properties are captured for small exemplars, using an image-based technique. Second, we develop an interactive solution to solve the problems of performing the transfer over both a large change in scale and to the different materials contained in a complete façade. Aiming to completely automate this process, a novel algorithm to differentiate between materials in the façade and associate them with the correct exemplars is introduced with promising results. Third, we present a new method for texture reconstruction from multiple images that optimises texture quality, by choosing the best view for every point and minimising seams. Material properties are transferred from the exemplars to the texture map, approximating reflectance and meso-structure. The combination of these techniques results in a complete working system capable of producing realistic relightable models of full building façades, containing high-resolution geometry and plausible visual appearance.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Experimental and Data-driven Workflows for Microstructure-based Damage Prediction

Materialermüdung ist die häufigste Ursache für mechanisches Versagen. Die Degradationsmechanismen, welche die Lebensdauer von Bauteilen bei vergleichsweise ausgeprägten zyklischen Belastungen bestimmen, sind gut bekannt. Bei Belastungen im makroskopisch elastischen Bereich hingegen, der (sehr) hochzyklischen Ermüdung, bestimmen die innere Struktur eines Werkstoffs und die Wechselwirkung kristallografischer Defekte die Lebensdauer. Unter diesen Umständen sind die inneren Degradationsphänomene auf der mikroskopischen Skala weitgehend reversibel und führen nicht zur Bildung kritischer Schädigungen, die kontinuierlich wachsen können. Allerdings sind einige Kornensembles in polykristallinen Metallen, je nach den lokalen mikrostrukturellen Gegebenheiten, anfällig für Schädigungsinitiierung, Rissbildung und -wachstum und wirken daher als Schwachstellen. Daher weisen Bauteile, die solchen Belastungen ausgesetzt sind, oft eine ausgeprägte Lebensdauerstreuung auf. Die Tatsache, dass ein umfassendes mechanistisches Verständnis für diese Degradationsprozesse in verschiedenen Werkstoffen nicht vorliegt, hat zur Folge, dass die derzeitigen Modellierungsbemühungen die mittlere Lebensdauer und ihre Varianz in der Regel nur mit unbefriedigender Genauigkeit vorhersagen. Dies wiederum erschwert die Bauteilauslegung und macht die Nutzung von Sicherheitsfaktoren während des Dimensionierungsprozesses erforderlich. Abhilfe kann geschaffen werden, indem umfangreiche Daten zu Einflussfaktoren und deren Wirkung auf die Bildung initialer Ermüdungsschädigungen erhoben werden. Die Datenknappheit wirkt sich nach wie vor negativ auf Datenwissenschaftler und Modellierungsexperten aus, die versuchen, trotz geringer Stichprobengröße und unvollständigen Merkmalsräumen, mikrostrukturelle Abhängigkeiten abzuleiten, datengetriebene Vorhersagemodelle zu trainieren oder physikalische, regelbasierte Modelle zu parametrisieren. Die Tatsache, dass nur wenige kritische Schädigungen bezogen auf das gesamte Probenvolumen auftreten und die hochzyklische Ermüdung eine Vielzahl unterschiedlicher Abhängigkeiten aufweist, impliziert einige Anforderungen an die Datenerfassung und -verarbeitung. Am wichtigsten ist, dass die Messtechniken so empfindlich sind, dass nuancierte Schwankungen im Probenzustand erfasst werden können, dass die gesamte Routine effizient ist und dass die korrelative Mikroskopie räumliche Informationen aus verschiedenen Messungen miteinander verbindet. Das Hauptziel dieser Arbeit besteht darin, einen Workflow zu etablieren, der den Datenmangel behebt, so dass die zukünftige virtuelle Auslegung von Komponenten effizienter, zuverlässiger und nachhaltiger gestaltet werden kann. Zu diesem Zweck wird in dieser Arbeit ein kombinierter experimenteller und datenverarbeitender Workflow vorgeschlagen, um multimodale Datensätze zu Ermüdungsschädigungen zu erzeugen. Der Schwerpunkt liegt dabei auf dem Auftreten von lokalen Gleitbändern, der Rissinitiierung und dem Wachstum mikrostrukturell kurzer Risse. Der Workflow vereint die Ermüdungsprüfung von mesoskaligen Proben, um die Empfindlichkeit der Schädigungsdetektion zu erhöhen, die ergänzende Charakterisierung, die multimodale Registrierung und Datenfusion der heterogenen Daten, sowie die bildverarbeitungsbasierte Schädigungslokalisierung und -bewertung. Mesoskalige Biegeresonanzprüfung ermöglicht das Erreichen des hochzyklischen Ermüdungszustands in vergleichsweise kurzen Zeitspannen bei gleichzeitig verbessertem Auflösungsvermögen der Schädigungsentwicklung. Je nach Komplexität der einzelnen Bildverarbeitungsaufgaben und Datenverfügbarkeit werden entweder regelbasierte Bildverarbeitungsverfahren oder Repräsentationslernen gezielt eingesetzt. So sorgt beispielsweise die semantische Segmentierung von Schädigungsstellen dafür, dass wichtige Ermüdungsmerkmale aus mikroskopischen Abbildungen extrahiert werden können. Entlang des Workflows wird auf einen hohen Automatisierungsgrad Wert gelegt. Wann immer möglich, wurde die Generalisierbarkeit einzelner Workflow-Elemente untersucht. Dieser Workflow wird auf einen ferritischen Stahl (EN 1.4003) angewendet. Der resultierende Datensatz verknüpft unter anderem große verzerrungskorrigierte Mikrostrukturdaten mit der Schädigungslokalisierung und deren zyklischer Entwicklung. Im Zuge der Arbeit wird der Datensatz wird im Hinblick auf seinen Informationsgehalt untersucht, indem detaillierte, analytische Studien zur einzelnen Schädigungsbildung durchgeführt werden. Auf diese Weise konnten unter anderem neuartige, quantitative Erkenntnisse über mikrostrukturinduzierte plastische Verformungs- und Rissstopmechanismen gewonnen werden. Darüber hinaus werden aus dem Datensatz abgeleitete kornweise Merkmalsvektoren und binäre Schädigungskategorien verwendet, um einen Random-Forest-Klassifikator zu trainieren und dessen Vorhersagegüte zu bewerten. Der vorgeschlagene Workflow hat das Potenzial, die Grundlage für künftiges Data Mining und datengetriebene Modellierung mikrostrukturempfindlicher Ermüdung zu legen. Er erlaubt die effiziente Erhebung statistisch repräsentativer Datensätze mit gleichzeitig hohem Informationsgehalt und kann auf eine Vielzahl von Werkstoffen ausgeweitet werden

On object recognition for industrial augmented reality

Some reasons are market pressure, an increase of functionality, and adaptability to an already complex environment, among others. Therefore, workers face fast-changing and challenging tasks along with all the product lifecycle that reach the human cognitive limits. Although nowadays some operations are automated, many of them still need to be carried out by humans because of their complexity. In addition to management strategies and design for X, Industrial Augmented Reality (IAR) has proven to potentially benefit activities such as maintenance, assembly, manufacturing, and repair, among others. It is also supposed to upgrade the manufacturing processes by improving it, simplifying decision-making activities, reducing time and user movements, diminishing errors, and decreasing mental and physical effort. Nevertheless, IAR has not succeeded in breaking out of the laboratories and establishing itself as a strong solution in the industry, mainly because technical and interaction components are far from ideal. Its advance is limited by its enabling technologies. One of its biggest challenges are the methods for understanding the surroundings considering the different domain variables that affect IAR implementations. Thus, inspired by some systematical methodologies proposing that, for any problemsolving activity, it is required to define the characteristics that constrain the problem and the needs to be satisfied, a general frame of IAR was proposed through the identification of Domain Variables (DV), that are relevant characteristics of the industrial process in the previous Augmented Reality (AR) applications. These DV regard the user, parts, environment, and task that have an impact on the technical implementation and user performance and perception (Chapter 2). Subsequently, a detailed analysis of the influence of the DV on technical implementations related to the processes intended to understand the surroundings was performed. The results of this analysis suggest that the DV influence the technical process in two ways. The first one is that they define the boundaries in the characteristics of the technology, and the second one is that they cause some issues in the process of understanding the surroundings (Chapter 3). Further, an automatic method for creating synthetic datasets using solely the 3D model of the parts was proposed. It is hypothesized that the proposed variables are the main source of visual variations of an object in this context. Thus, the proposed method is derived from physically recreated light-matter interactions of this relevant variables. This method is aimed to create fully labeled datasets for training and testing surrounding understanding algorithms (Chapter 4). Finally, the proposed method is evaluated in a study case of object classification of two cases: a particular industrial case, and a general classification problem (using classes of ImageNet). Results suggest that fine-tuning models with the proposed method reach comparable performance (no statistical difference) than models trained with photos. These results validate the proposed method as a viable alternative for training surrounding understanding algorithms applied to industrial cases (Chapter 5)

Stereo vision based on compressed feature correlation and graph cut

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.Includes bibliographical references (p. 131-145).This dissertation has developed a fast and robust algorithm to solve the dense correspondence problem with a good performance in untextured regions by merging Sparse Array Correlation from the computational fluids community into graph cut from the computer vision community. The proposed methodology consists of two independent modules. The first module is named Compressed Feature Correlation which is originated from Particle Image Velocimetry (PIV). The algorithm uses an image compression scheme that retains pixel values in high-intensity gradient areas while eliminating pixels with little correlation information in smooth surface regions resulting in a highly reduced image datasets. In addition, by utilizing an error correlation function, pixel comparisons are made through single integer calculations eliminating time consuming multiplication and floating point arithmetic. Unlike the traditional fixed window sorting scheme, adaptive correlation window positioning is implemented by dynamically placing strong features at the center of each correlation window. A confidence measure is developed to validate correlation outputs. The sparse depth map generated by this ultra-fast Compressed Feature Correlation may either serve as inputs to global methods or be interpolated into dense depth map when object boundaries are clearly defined. The second module enables a modified graph cut algorithm with an improved energy model that accepts prior information by fixing data energy penalties. The image pixels with known disparity values stabilize and speed up global optimization. As a result less iterations are necessary and sensitivity to parameters is reduced.(cont.) An efficient hybrid approach is implemented based on the above two modules. By coupling a simpler and much less expensive algorithm, Compressed Feature Correlation, with a more expensive algorithm, graph cut, the computational expense of the hybrid calculation is one third of performing the entire calculation using the more expensive of the two algorithms, while accuracy and robustness are improved at the same time. Qualitative and quantitative results on both simulated disparities and real stereo images are presented.by Sheng Sarah Tan.Ph.D

Augmented reality X-ray vision on optical see-through head mounted displays

Abstract. In this thesis, we present the development and evaluation of an augmented reality X-ray system on optical see-through head-mounted displays. Augmented reality X-ray vision allows users to see through solid surfaces such as walls and facades, by augmenting the real view with virtual images representing the hidden objects. Our system is developed based on the optical see-through mixed reality headset Microsoft Hololens. We have developed an X-ray cutout algorithm that uses the geometric data of the environment and enables seeing through surfaces. We have developed four different visualizations as well based on the algorithm. The first visualization renders simply the X-ray cutout without displaying any information about the occluding surface. The other three visualizations display features extracted from the occluder surface to help the user to get better depth perception of the virtual objects. We have used Sobel edge detection to extract the information. The three visualizations differ in the way to render the extracted features. A subjective experiment is conducted to test and evaluate the visualizations and to compare them with each other. The experiment consists of two parts; depth estimation task and a questionnaire. Both the experiment and its results are presented in the thesis