Recovering refined surface normals for relighting clothing in dynamic scenes

In this paper we present a method to relight captured 3D video sequences of non-rigid, dynamic scenes, such as clothing of real actors, reconstructed from multiple view video. A view-dependent approach is introduced to refine an initial coarse surface reconstruction using shape-from-shading to estimate detailed surface normals. The prior surface approximation is used to constrain the simultaneous estimation of surface normals and scene illumination, under the assumption of Lambertian surface reflectance. This approach enables detailed surface normals of a moving non-rigid object to be estimated from a single image frame. Refined normal estimates from multiple views are integrated into a single surface normal map. This approach allows highly non-rigid surfaces, such as creases in clothing, to be relit whilst preserving the detailed dynamics observed in video

Image-based recognition, 3D localization, and retro-reflectivity evaluation of high-quantity low-cost roadway assets for enhanced condition assessment

Systematic condition assessment of high-quantity low-cost roadway assets such as traffic signs, guardrails, and pavement markings requires frequent reporting on location and up-to-date status of these assets. Today, most Departments of Transportation (DOTs) in the US collect data using camera-mounted vehicles to filter, annotate, organize, and present the data necessary for these assessments. However, the cost and complexity of the collection, analysis, and reporting as-is conditions result in sparse and infrequent monitoring. Thus, some of the gains in efficiency are consumed by monitoring costs. This dissertation proposes to improve frequency, detail, and applicability of image-based condition assessment via automating detection, classification, and 3D localization of multiple types of high-quantity low-cost roadway assets using both images collected by the DOTs and online databases such Google Street View Images. To address the new requirements of US Federal Highway Administration (FHWA), a new method is also developed that simulates nighttime visibility of traffic signs from images taken during daytime and measures their retro-reflectivity condition. To initiate detection and classification of high-quantity low-cost roadway assets from street-level images, a number of algorithms are proposed that automatically segment and localize high-level asset categories in 3D. The first set of algorithms focus on the task of detecting and segmenting assets at high-level categories. More specifically, a method based on Semantic Texton Forest classifiers, segments each geo-registered 2D video frame at the pixel-level based on shape, texture, and color. A Structure from Motion (SfM) procedure reconstructs the road and its assets in 3D. Next, a voting scheme assigns the most observed asset category to each point in 3D. The experimental results from application of this method are promising, nevertheless because this method relies on using supervised ground-truth pixel labels for training purposes, scaling it to various types of assets is challenging. To address this issue, a non-parametric image parsing method is proposed that leverages lazy learning scheme for segmentation and recognition of roadway assets. The semi-supervised technique used in the proposed method does not need training and provides ground truth data in a more efficient manner. It is easily scalable to thousands of video frames captured during data collection. Once the high-level asset categories are detected, specific techniques needs to be exploited to detect and classify the assets at a higher level of granularity. To this end, performance of three computer vision algorithms are evaluated for classification of traffic signs in presence of cluttered backgrounds and static and dynamic occlusions. Without making any prior assumptions about the location of traffic signs in 2D, the best performing method uses histograms of oriented gradients and color together with multiple one-vs-all Support Vector Machines, and classifies these assets into warning, regulatory, stop, and yield sign categories. To minimize the reliance on visual data collected by the DOTs and improve frequency and applicability of condition assessment, a new end-to-end procedure is presented that applies the above algorithms and creates comprehensive inventory of traffic signs using Google Street View images. By processing images extracted using Google Street View API and discriminative classification scores from all images that see a sign, the most probable 3D location of each traffic sign is derived and is shown on the Google Earth using a dynamic heat map. A data card containing information about location, type, and condition of each detected traffic sign is also created. Finally, a computer vision-based algorithm is proposed that measures retro-reflectivity of traffic signs during daytime using a vehicle mounted device. The algorithm simulates nighttime visibility of traffic signs from images taken during daytime and measures their retro-reflectivity. The technique is faster, cheaper, and safer compared to the state-of-the-art as it neither requires nighttime operation nor requires manual sign inspection. It also satisfies measurement guidelines set forth by FHWA both in terms of granularity and accuracy. To validate the techniques, new detailed video datasets and their ground-truth were generated from 2.2-mile smart road research facility and two interstate highways in the US. The comprehensive dataset contains over 11,000 annotated U.S. traffic sign images and exhibits large variations in sign pose, scale, background, illumination, and occlusion conditions. The performance of all algorithms were examined using these datasets. For retro-reflectivity measurement of traffic signs, experiments were conducted at different times of day and for different distances. Results were compared with a method recommended by ASTM standards. The experimental results show promise in scalability of these methods to reduce the time and effort required for developing road inventories, especially for those assets such as guardrails and traffic lights that are not typically considered in 2D asset recognition methods and also multiple categories of traffic signs. The applicability of Google Street View Images for inventory management purposes and also the technique for retro-reflectivity measurement during daytime demonstrate strong potential in lowering inspection costs and improving safety in practical applications

Face Normals "in-the- wild" using Fully Convolutional Networks

In this work we pursue a data-driven approach to the problem of estimating surface normals from a single intensity image, focusing in particular on human faces. We introduce new methods to exploit the currently available facial databases for dataset construction and tailor a deep convolutional neural network to the task of estimating facial surface normals in-the-wild. We train a fully convolutional network that can accurately recover facial normals from images including a challenging variety of expressions and facial poses. We compare against state-of-the-art face Shape-from-Shading and 3D reconstruction techniques and show that the proposed network can recover substantially more accurate and realistic normals. Furthermore, in contrast to other existing face-specific surface recovery methods, we do not require the solving of an explicit alignment step due to the fully convolutional nature of our network

New editing techniques for video post-processing

This thesis contributes to capturing 3D cloth shape, editing cloth texture and altering object shape and motion in multi-camera and monocular video recordings. We propose a technique to capture cloth shape from a 3D scene flow by determining optical flow in several camera views. Together with a silhouette matching constraint we can track and reconstruct cloth surfaces in long video sequences. In the area of garment motion capture, we present a system to reconstruct time-coherent triangle meshes from multi-view video recordings. Texture mapping of the acquired triangle meshes is used to replace the recorded texture with new cloth patterns. We extend this work to the more challenging single camera view case. Extracting texture deformation and shading effects simultaneously enables us to achieve texture replacement effects for garments in monocular video recordings. Finally, we propose a system for the keyframe editing of video objects. A color-based segmentation algorithm together with automatic video inpainting for filling in missing background texture allows us to edit the shape and motion of 2D video objects. We present examples for altering object trajectories, applying non-rigid deformation and simulating camera motion.In dieser Dissertation stellen wir Beiträge zur 3D-Rekonstruktion von Stoffoberfächen, zum Editieren von Stofftexturen und zum Editieren von Form und Bewegung von Videoobjekten in Multikamera- und Einkamera-Aufnahmen vor. Wir beschreiben eine Methode für die 3D-Rekonstruktion von Stoffoberflächen, die auf der Bestimmung des optischen Fluß in mehreren Kameraansichten basiert. In Kombination mit einem Abgleich der Objektsilhouetten im Video und in der Rekonstruktion erhalten wir Rekonstruktionsergebnisse für längere Videosequenzen. Für die Rekonstruktion von Kleidungsstücken beschreiben wir ein System, das zeitlich kohärente Dreiecksnetze aus Multikamera-Aufnahmen rekonstruiert. Mittels Texturemapping der erhaltenen Dreiecksnetze wird die Stofftextur in der Aufnahme mit neuen Texturen ersetzt. Wir setzen diese Arbeit fort, indem wir den anspruchsvolleren Fall mit nur einer einzelnen Videokamera betrachten. Um realistische Resultate beim Ersetzen der Textur zu erzielen, werden sowohl Texturdeformationen durch zugrundeliegende Deformation der Oberfläche als auch Beleuchtungseffekte berücksichtigt. Im letzten Teil der Dissertation stellen wir ein System zum Editieren von Videoobjekten mittels Keyframes vor. Dies wird durch eine Kombination eines farbbasierten Segmentierungsalgorithmus mit automatischem Auffüllen des Hintergrunds erreicht, wodurch Form und Bewegung von 2D-Videoobjekten editiert werden können. Wir zeigen Beispiele für editierte Objekttrajektorien, beliebige Deformationen und simulierte Kamerabewegung

Material Recognition Meets 3D Reconstruction : Novel Tools for Efficient, Automatic Acquisition Systems

For decades, the accurate acquisition of geometry and reflectance properties has represented one of the major objectives in computer vision and computer graphics with many applications in industry, entertainment and cultural heritage. Reproducing even the finest details of surface geometry and surface reflectance has become a ubiquitous prerequisite in visual prototyping, advertisement or digital preservation of objects. However, today's acquisition methods are typically designed for only a rather small range of material types. Furthermore, there is still a lack of accurate reconstruction methods for objects with a more complex surface reflectance behavior beyond diffuse reflectance. In addition to accurate acquisition techniques, the demand for creating large quantities of digital contents also pushes the focus towards fully automatic and highly efficient solutions that allow for masses of objects to be acquired as fast as possible. This thesis is dedicated to the investigation of basic components that allow an efficient, automatic acquisition process. We argue that such an efficient, automatic acquisition can be realized when material recognition "meets" 3D reconstruction and we will demonstrate that reliably recognizing the materials of the considered object allows a more efficient geometry acquisition. Therefore, the main objectives of this thesis are given by the development of novel, robust geometry acquisition techniques for surface materials beyond diffuse surface reflectance, and the development of novel, robust techniques for material recognition. In the context of 3D geometry acquisition, we introduce an improvement of structured light systems, which are capable of robustly acquiring objects ranging from diffuse surface reflectance to even specular surface reflectance with a sufficient diffuse component. We demonstrate that the resolution of the reconstruction can be increased significantly for multi-camera, multi-projector structured light systems by using overlappings of patterns that have been projected under different projector poses. As the reconstructions obtained by applying such triangulation-based techniques still contain high-frequency noise due to inaccurately localized correspondences established for images acquired under different viewpoints, we furthermore introduce a novel geometry acquisition technique that complements the structured light system with additional photometric normals and results in significantly more accurate reconstructions. In addition, we also present a novel method to acquire the 3D shape of mirroring objects with complex surface geometry. The aforementioned investigations on 3D reconstruction are accompanied by the development of novel tools for reliable material recognition which can be used in an initial step to recognize the present surface materials and, hence, to efficiently select the subsequently applied appropriate acquisition techniques based on these classified materials. In the scope of this thesis, we therefore focus on material recognition for scenarios with controlled illumination as given in lab environments as well as scenarios with natural illumination that are given in photographs of typical daily life scenes. Finally, based on the techniques developed in this thesis, we provide novel concepts towards efficient, automatic acquisition systems

Quantifying Texture Scale in Accordance With Human Perception

Visual texture has multiple perceptual attributes (e.g. regularity, isotropy, etc.), including scale. The scale of visual texture has been defined as the size of the repeating unit (or texel) of which the texture is composed. Not all textures are formed through the placement of a clearly discernible repeating unit (e.g. irregular and stochastic textures). There is currently no rigorous definition for texture scale that is applicable to textures of a wide range of regularities. We hypothesised that texture scale ought to extend to these less regular textures. Non-overlapping sample windows (or patches) taken from a texture appear increasingly similar as the size of the window gets larger. This is true irrespective of whether the texture is formed by the placement of a discernible repeating unit or not. We propose the following new characterisation for texture scale: “the smallest window size beyond within which texture appears consistently”. We perform two psychophysical studies and report data that demonstrates consensus across subjects and across methods of probing in the assessment of texture scale. We then present an empirical algorithm for the estimation of scale based on this characterisation. We demonstrate agreement between the algorithm and (subjective) human assessment with an RMS accuracy of 1.2 just-noticeable-differences, a significant improvement over previous published algorithms. We provide two ground-truth perceptual datasets, one for each of our psychophysical studies, for the texture scale of the entire Brodatz album, together with confidence levels for each of our estimates. Finally, we make available an online tool which researchers can use to obtain texture scale estimates by uploading images of textures

Bayesian Optimization for Image Segmentation, Texture Flow Estimation and Image Deblurring

Ph.DDOCTOR OF PHILOSOPH

Inverse problem theory in shape and action modeling

In this thesis we consider shape and action modeling problems under the perspective of inverse problem theory. Inverse problem theory proposes a mathematical framework for solving model parameter estimation problems. Inverse problems are typically ill-posed, which makes their solution challenging. Regularization theory and Bayesian statistical methods, which are proposed in the context of inverse problem theory, provide suitable methods for dealing with ill-posed problems. Regarding the application of inverse problem theory in shape and action modeling, we first discuss the problem of saliency prediction, considering a model proposed by the coherence theory of attention. According to coherence theory, salience regions emerge via proto-objects which we model using harmonic functions (thin-membranes). We also discuss the modeling of the 3D scene, as it is fundamental for extracting suitable scene features, which guide the generation of proto-objects. The next application we consider is the problem of image fusion. In this context, we propose a variational image fusion framework, based on confidence driven total variation regularization, and we consider its application to the problem of depth image fusion, which is an important step in the dense 3D scene reconstruction pipeline. The third problem we encounter regards action modeling, and in particular the recognition of human actions based on 3D data. Here, we employ a Bayesian nonparametric model to capture the idiosyncratic motions of the different body parts. Recognition is achieved by comparing the motion behaviors of the subject to a dictionary of behaviors for each action, learned by examples collected from other subjects. Next, we consider the 3D modeling of articulated objects from images taken from the web, with application to the 3D modeling of animals. By decomposing the full object in rigid components and by considering different aspects of these components, we model the object up this hierarchy, in order to obtain a 3D model of the entire object. Single view 3D modeling as well as model registration is performed, based on regularization methods. The last problem we consider, is the modeling of 3D specular (non-Lambertian) surfaces from a single image. To solve this challenging problem we propose a Bayesian non-parametric model for estimating the normal field of the surface from its appearance, by identifying the material of the surface. After computing an initial model of the surface, we apply regularization of its normal field considering also a photo-consistency constraint, in order to estimate the final shape of the surface. Finally, we conclude this thesis by summarizing the most significant results and by suggesting future directions regarding the application of inverse problem theory to challenging computer vision problems, as the ones encountered in this work