From small to large baseline multiview stereo : dealing with blur, clutter and occlusions

This thesis addresses the problem of reconstructing the three-dimensional (3D) digital model of a scene from a collection of two-dimensional (2D) images taken from it. To address this fundamental computer vision problem, we propose three algorithms. They are the main contributions of this thesis. First, we solve multiview stereo with the o -axis aperture camera. This system has a very small baseline as images are captured from viewpoints close to each other. The key idea is to change the size or the 3D location of the aperture of the camera so as to extract selected portions of the scene. Our imaging model takes both defocus and stereo information into account and allows to solve shape reconstruction and image restoration in one go. The o -axis aperture camera can be used in a small-scale space where the camera motion is constrained by the surrounding environment, such as in 3D endoscopy. Second, to solve multiview stereo with large baseline, we present a framework that poses the problem of recovering a 3D surface in the scene as a regularized minimal partition problem of a visibility function. The formulation is convex and hence guarantees that the solution converges to the global minimum. Our formulation is robust to view-varying extensive occlusions, clutter and image noise. At any stage during the estimation process the method does not rely on the visual hull, 2D silhouettes, approximate depth maps, or knowing which views are dependent(i.e., overlapping) and which are independent( i.e., non overlapping). Furthermore, the degenerate solution, the null surface, is not included as a global solution in this formulation. One limitation of this algorithm is that its computation complexity grows with the number of views that we combine simultaneously. To address this limitation, we propose a third formulation. In this formulation, the visibility functions are integrated within a narrow band around the estimated surface by setting weights to each point along optical rays. This thesis presents technical descriptions for each algorithm and detailed analyses to show how these algorithms improve existing reconstruction techniques

Realtime State Estimation with Tactile and Visual sensing. Application to Planar Manipulation

Accurate and robust object state estimation enables successful object manipulation. Visual sensing is widely used to estimate object poses. However, in a cluttered scene or in a tight workspace, the robot's end-effector often occludes the object from the visual sensor. The robot then loses visual feedback and must fall back on open-loop execution. In this paper, we integrate both tactile and visual input using a framework for solving the SLAM problem, incremental smoothing and mapping (iSAM), to provide a fast and flexible solution. Visual sensing provides global pose information but is noisy in general, whereas contact sensing is local, but its measurements are more accurate relative to the end-effector. By combining them, we aim to exploit their advantages and overcome their limitations. We explore the technique in the context of a pusher-slider system. We adapt iSAM's measurement cost and motion cost to the pushing scenario, and use an instrumented setup to evaluate the estimation quality with different object shapes, on different surface materials, and under different contact modes

Real-time 3D hand reconstruction in challenging scenes from a single color or depth camera

Hands are one of the main enabling factors for performing complex tasks and humans naturally use them for interactions with their environment. Reconstruction and digitization of 3D hand motion opens up many possibilities for important applications. Hands gestures can be directly used for human–computer interaction, which is especially relevant for controlling augmented or virtual reality (AR/VR) devices where immersion is of utmost importance. In addition, 3D hand motion capture is a precondition for automatic sign-language translation, activity recognition, or teaching robots. Different approaches for 3D hand motion capture have been actively researched in the past. While being accurate, gloves and markers are intrusive and uncomfortable to wear. Hence, markerless hand reconstruction based on cameras is desirable. Multi-camera setups provide rich input, however, they are hard to calibrate and lack the flexibility for mobile use cases. Thus, the majority of more recent methods uses a single color or depth camera which, however, makes the problem harder due to more ambiguities in the input. For interaction purposes, users need continuous control and immediate feedback. This means the algorithms have to run in real time and be robust in uncontrolled scenes. These requirements, achieving 3D hand reconstruction in real time from a single camera in general scenes, make the problem significantly more challenging. While recent research has shown promising results, current state-of-the-art methods still have strong limitations. Most approaches only track the motion of a single hand in isolation and do not take background-clutter or interactions with arbitrary objects or the other hand into account. The few methods that can handle more general and natural scenarios run far from real time or use complex multi-camera setups. Such requirements make existing methods unusable for many aforementioned applications. This thesis pushes the state of the art for real-time 3D hand tracking and reconstruction in general scenes from a single RGB or depth camera. The presented approaches explore novel combinations of generative hand models, which have been used successfully in the computer vision and graphics community for decades, and powerful cutting-edge machine learning techniques, which have recently emerged with the advent of deep learning. In particular, this thesis proposes a novel method for hand tracking in the presence of strong occlusions and clutter, the first method for full global 3D hand tracking from in-the-wild RGB video, and a method for simultaneous pose and dense shape reconstruction of two interacting hands that, for the first time, combines a set of desirable properties previously unseen in the literature.Hände sind einer der Hauptfaktoren für die Ausführung komplexer Aufgaben, und Menschen verwenden sie auf natürliche Weise für Interaktionen mit ihrer Umgebung. Die Rekonstruktion und Digitalisierung der 3D-Handbewegung eröffnet viele Möglichkeiten für wichtige Anwendungen. Handgesten können direkt als Eingabe für die Mensch-Computer-Interaktion verwendet werden. Dies ist insbesondere für Geräte der erweiterten oder virtuellen Realität (AR / VR) relevant, bei denen die Immersion von größter Bedeutung ist. Darüber hinaus ist die Rekonstruktion der 3D Handbewegung eine Voraussetzung zur automatischen Übersetzung von Gebärdensprache, zur Aktivitätserkennung oder zum Unterrichten von Robotern. In der Vergangenheit wurden verschiedene Ansätze zur 3D-Handbewegungsrekonstruktion aktiv erforscht. Handschuhe und physische Markierungen sind zwar präzise, aber aufdringlich und unangenehm zu tragen. Daher ist eine markierungslose Handrekonstruktion auf der Basis von Kameras wünschenswert. Multi-Kamera-Setups bieten umfangreiche Eingabedaten, sind jedoch schwer zu kalibrieren und haben keine Flexibilität für mobile Anwendungsfälle. Daher verwenden die meisten neueren Methoden eine einzelne Farb- oder Tiefenkamera, was die Aufgabe jedoch schwerer macht, da mehr Ambiguitäten in den Eingabedaten vorhanden sind. Für Interaktionszwecke benötigen Benutzer kontinuierliche Kontrolle und sofortiges Feedback. Dies bedeutet, dass die Algorithmen in Echtzeit ausgeführt werden müssen und robust in unkontrollierten Szenen sein müssen. Diese Anforderungen, 3D-Handrekonstruktion in Echtzeit mit einer einzigen Kamera in allgemeinen Szenen, machen das Problem erheblich schwieriger. Während neuere Forschungsarbeiten vielversprechende Ergebnisse gezeigt haben, weisen aktuelle Methoden immer noch Einschränkungen auf. Die meisten Ansätze verfolgen die Bewegung einer einzelnen Hand nur isoliert und berücksichtigen keine alltäglichen Umgebungen oder Interaktionen mit beliebigen Objekten oder der anderen Hand. Die wenigen Methoden, die allgemeinere und natürlichere Szenarien verarbeiten können, laufen nicht in Echtzeit oder verwenden komplexe Multi-Kamera-Setups. Solche Anforderungen machen bestehende Verfahren für viele der oben genannten Anwendungen unbrauchbar. Diese Dissertation erweitert den Stand der Technik für die Echtzeit-3D-Handverfolgung und -Rekonstruktion in allgemeinen Szenen mit einer einzelnen RGB- oder Tiefenkamera. Die vorgestellten Algorithmen erforschen neue Kombinationen aus generativen Handmodellen, die seit Jahrzehnten erfolgreich in den Bereichen Computer Vision und Grafik eingesetzt werden, und leistungsfähigen innovativen Techniken des maschinellen Lernens, die vor kurzem mit dem Aufkommen neuronaler Netzwerke entstanden sind. In dieser Arbeit werden insbesondere vorgeschlagen: eine neuartige Methode zur Handbewegungsrekonstruktion bei starken Verdeckungen und in unkontrollierten Szenen, die erste Methode zur Rekonstruktion der globalen 3D Handbewegung aus RGB-Videos in freier Wildbahn und die erste Methode zur gleichzeitigen Rekonstruktion von Handpose und -form zweier interagierender Hände, die eine Reihe wünschenwerter Eigenschaften komibiniert

Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking

With efficient appearance learning models, Discriminative Correlation Filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions. However, the existing DCF paradigm suffers from two major issues, i.e., spatial boundary effect and temporal filter degradation. To mitigate these challenges, we propose a new DCF-based tracking method. The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensional discriminative manifold. More specifically, we apply structured spatial sparsity constraints to multi-channel filers. Consequently, the process of learning spatial filters can be approximated by the lasso regularisation. To encourage temporal consistency, the filter model is restricted to lie around its historical value and updated locally to preserve the global structure in the manifold. Last, a unified optimisation framework is proposed to jointly select temporal consistency preserving spatial features and learn discriminative filters with the augmented Lagrangian method. Qualitative and quantitative evaluations have been conducted on a number of well-known benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123 and VOT2018. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art approaches

Towards Scene Understanding with Detailed 3D Object Representations

Current approaches to semantic image and scene understanding typically employ rather simple object representations such as 2D or 3D bounding boxes. While such coarse models are robust and allow for reliable object detection, they discard much of the information about objects' 3D shape and pose, and thus do not lend themselves well to higher-level reasoning. Here, we propose to base scene understanding on a high-resolution object representation. An object class - in our case cars - is modeled as a deformable 3D wireframe, which enables fine-grained modeling at the level of individual vertices and faces. We augment that model to explicitly include vertex-level occlusion, and embed all instances in a common coordinate frame, in order to infer and exploit object-object interactions. Specifically, from a single view we jointly estimate the shapes and poses of multiple objects in a common 3D frame. A ground plane in that frame is estimated by consensus among different objects, which significantly stabilizes monocular 3D pose estimation. The fine-grained model, in conjunction with the explicit 3D scene model, further allows one to infer part-level occlusions between the modeled objects, as well as occlusions by other, unmodeled scene elements. To demonstrate the benefits of such detailed object class models in the context of scene understanding we systematically evaluate our approach on the challenging KITTI street scene dataset. The experiments show that the model's ability to utilize image evidence at the level of individual parts improves monocular 3D pose estimation w.r.t. both location and (continuous) viewpoint.Comment: International Journal of Computer Vision (appeared online on 4 November 2014). Online version: http://link.springer.com/article/10.1007/s11263-014-0780-