Wide baseline stereo matching with convex bounded-distortion constraints

Finding correspondences in wide baseline setups is a challenging problem. Existing approaches have focused largely on developing better feature descriptors for correspondence and on accurate recovery of epipolar line constraints. This paper focuses on the challenging problem of finding correspondences once approximate epipolar constraints are given. We introduce a novel method that integrates a deformation model. Specifically, we formulate the problem as finding the largest number of corresponding points related by a bounded distortion map that obeys the given epipolar constraints. We show that, while the set of bounded distortion maps is not convex, the subset of maps that obey the epipolar line constraints is convex, allowing us to introduce an efficient algorithm for matching. We further utilize a robust cost function for matching and employ majorization-minimization for its optimization. Our experiments indicate that our method finds significantly more accurate maps than existing approaches

CODE: Coherence based decision boundaries for feature correspondence

A key challenge in feature correspondence is the difficulty in differentiating true and false matches at a local descriptor level. This forces adoption of strict similarity thresholds that discard many true matches. However, if analyzed at a global level, false matches are usually randomly scattered while true matches tend to be coherent (clustered around a few dominant motions), thus creating a coherence based separability constraint. This paper proposes a non-linear regression technique that can discover such a coherence based separability constraint from highly noisy matches and embed it into a correspondence likelihood model. Once computed, the model can filter the entire set of nearest neighbor matches (which typically contains over 90 percent false matches) for true matches. We integrate our technique into a full feature correspondence system which reliably generates large numbers of good quality correspondences over wide baselines where previous techniques provide few or no matches

Robust surface modelling of visual hull from multiple silhouettes

Reconstructing depth information from images is one of the actively researched themes in computer vision and its application involves most vision research areas from object recognition to realistic visualisation. Amongst other useful vision-based reconstruction techniques, this thesis extensively investigates the visual hull (VH) concept for volume approximation and its robust surface modelling when various views of an object are available. Assuming that multiple images are captured from a circular motion, projection matrices are generally parameterised in terms of a rotation angle from a reference position in order to facilitate the multi-camera calibration. However, this assumption is often violated in practice, i.e., a pure rotation in a planar motion with accurate rotation angle is hardly realisable. To address this problem, at first, this thesis proposes a calibration method associated with the approximate circular motion. With these modified projection matrices, a resulting VH is represented by a hierarchical tree structure of voxels from which surfaces are extracted by the Marching cubes (MC) algorithm. However, the surfaces may have unexpected artefacts caused by a coarser volume reconstruction, the topological ambiguity of the MC algorithm, and imperfect image processing or calibration result. To avoid this sensitivity, this thesis proposes a robust surface construction algorithm which initially classifies local convex regions from imperfect MC vertices and then aggregates local surfaces constructed by the 3D convex hull algorithm. Furthermore, this thesis also explores the use of wide baseline images to refine a coarse VH using an affine invariant region descriptor. This improves the quality of VH when a small number of initial views is given. In conclusion, the proposed methods achieve a 3D model with enhanced accuracy. Also, robust surface modelling is retained when silhouette images are degraded by practical noise

Robust surface modelling of visual hull from multiple silhouettes

Reconstructing depth information from images is one of the actively researched themes in computer vision and its application involves most vision research areas from object recognition to realistic visualisation. Amongst other useful vision-based reconstruction techniques, this thesis extensively investigates the visual hull (VH) concept for volume approximation and its robust surface modelling when various views of an object are available. Assuming that multiple images are captured from a circular motion, projection matrices are generally parameterised in terms of a rotation angle from a reference position in order to facilitate the multi-camera calibration. However, this assumption is often violated in practice, i.e., a pure rotation in a planar motion with accurate rotation angle is hardly realisable. To address this problem, at first, this thesis proposes a calibration method associated with the approximate circular motion. With these modified projection matrices, a resulting VH is represented by a hierarchical tree structure of voxels from which surfaces are extracted by the Marching cubes (MC) algorithm. However, the surfaces may have unexpected artefacts caused by a coarser volume reconstruction, the topological ambiguity of the MC algorithm, and imperfect image processing or calibration result. To avoid this sensitivity, this thesis proposes a robust surface construction algorithm which initially classifies local convex regions from imperfect MC vertices and then aggregates local surfaces constructed by the 3D convex hull algorithm. Furthermore, this thesis also explores the use of wide baseline images to refine a coarse VH using an affine invariant region descriptor. This improves the quality of VH when a small number of initial views is given. In conclusion, the proposed methods achieve a 3D model with enhanced accuracy. Also, robust surface modelling is retained when silhouette images are degraded by practical noise

Shape Animation with Combined Captured and Simulated Dynamics

We present a novel volumetric animation generation framework to create new types of animations from raw 3D surface or point cloud sequence of captured real performances. The framework considers as input time incoherent 3D observations of a moving shape, and is thus particularly suitable for the output of performance capture platforms. In our system, a suitable virtual representation of the actor is built from real captures that allows seamless combination and simulation with virtual external forces and objects, in which the original captured actor can be reshaped, disassembled or reassembled from user-specified virtual physics. Instead of using the dominant surface-based geometric representation of the capture, which is less suitable for volumetric effects, our pipeline exploits Centroidal Voronoi tessellation decompositions as unified volumetric representation of the real captured actor, which we show can be used seamlessly as a building block for all processing stages, from capture and tracking to virtual physic simulation. The representation makes no human specific assumption and can be used to capture and re-simulate the actor with props or other moving scenery elements. We demonstrate the potential of this pipeline for virtual reanimation of a real captured event with various unprecedented volumetric visual effects, such as volumetric distortion, erosion, morphing, gravity pull, or collisions

Robust Wide-Baseline Stereo Matching for Sparsely Textured Scenes

The task of wide baseline stereo matching algorithms is to identify corresponding elements in pairs of overlapping images taken from significantly different viewpoints. Such algorithms are a key ingredient to many computer vision applications, including object recognition, automatic camera orientation, 3D reconstruction and image registration. Although today's methods for wide baseline stereo matching produce reliable results for typical application scenarios, they assume properties of the image data that are not always granted, for example a significant amount of distinctive surface texture. For such problems, highly advanced algorithms have been proposed, which are often very problem specific, difficult to implement and hard to transfer to new matching problems. The motivation for our work comes from the belief that we can find a generic formulation for robust wide baseline image matching that is able to solve difficult matching problems and at the same time applicable to a variety of applications. It should be easy to implement, and have good semantic interpretability. Therefore our key contribution is the development of a generic statistical model for wide baseline stereo matching, which seamlessly integrates different types of image features, similarity measures and spatial feature relationships as information cues. It unifies the ideas of existing approaches into a Bayesian formulation, which has a clear statistical interpretation as the MAP estimate of a binary classification problem. The model ultimately takes the form of a global minimization problem that can be solved with standard optimization techniques. The particular type of features, measures, and spatial relationships however is not prescribed. A major advantage of our model over existing approaches is its ability to compensate weaknesses in one information cue implicitly by exploiting the strength of others. In our experiments we concentrate on images of sparsely textured scenes as a specifically difficult matching problem. Here the amount of stable image features is typically rather small, and the distinctiveness of feature descriptions often low. We use the proposed framework to implement a wide baseline stereo matching algorithm that can deal better with poor texture than established methods. For demonstrating the practical relevance, we also apply this algorithm to a system for automatic image orientation. Here, the task is to reconstruct the relative 3D positions and orientations of the cameras corresponding to a set of overlapping images. We show that our implementation leads to more successful results in case of sparsely textured scenes, while still retaining state of the art performance on standard datasets.Robuste Merkmalszuordnung für Bildpaare schwach texturierter Szenen mit deutlicher Stereobasis Die Aufgabe von Wide Baseline Stereo Matching Algorithmen besteht darin, korrespondierende Elemente in Paaren überlappender Bilder mit deutlich verschiedenen Kamerapositionen zu bestimmen. Solche Algorithmen sind ein grundlegender Baustein für zahlreiche Computer Vision Anwendungen wie Objekterkennung, automatische Kameraorientierung, 3D Rekonstruktion und Bildregistrierung. Die heute etablierten Verfahren für Wide Baseline Stereo Matching funktionieren in typischen Anwendungsszenarien sehr zuverlässig. Sie setzen jedoch Eigenschaften der Bilddaten voraus, die nicht immer gegeben sind, wie beispielsweise einen hohen Anteil markanter Textur. Für solche Fälle wurden sehr komplexe Verfahren entwickelt, die jedoch oft nur auf sehr spezifische Probleme anwendbar sind, einen hohen Implementierungsaufwand erfordern, und sich zudem nur schwer auf neue Matchingprobleme übertragen lassen. Die Motivation für diese Arbeit entstand aus der Überzeugung, dass es eine möglichst allgemein anwendbare Formulierung für robustes Wide Baseline Stereo Matching geben muß, die sich zur Lösung schwieriger Zuordnungsprobleme eignet und dennoch leicht auf verschiedenartige Anwendungen angepasst werden kann. Sie sollte leicht implementierbar sein und eine hohe semantische Interpretierbarkeit aufweisen. Unser Hauptbeitrag besteht daher in der Entwicklung eines allgemeinen statistischen Modells für Wide Baseline Stereo Matching, das verschiedene Typen von Bildmerkmalen, Ähnlichkeitsmaßen und räumlichen Beziehungen nahtlos als Informationsquellen integriert. Es führt Ideen bestehender Lösungsansätze in einer Bayes'schen Formulierung zusammen, die eine klare Interpretation als MAP Schätzung eines binären Klassifikationsproblems hat. Das Modell nimmt letztlich die Form eines globalen Minimierungsproblems an, das mit herkömmlichen Optimierungsverfahren gelöst werden kann. Der konkrete Typ der verwendeten Bildmerkmale, Ähnlichkeitsmaße und räumlichen Beziehungen ist nicht explizit vorgeschrieben. Ein wichtiger Vorteil unseres Modells gegenüber vergleichbaren Verfahren ist seine Fähigkeit, Schwachpunkte einer Informationsquelle implizit durch die Stärken anderer Informationsquellen zu kompensieren. In unseren Experimenten konzentrieren wir uns insbesondere auf Bilder schwach texturierter Szenen als ein Beispiel schwieriger Zuordnungsprobleme. Die Anzahl stabiler Bildmerkmale ist hier typischerweise gering, und die Unterscheidbarkeit der Merkmalsbeschreibungen schlecht. Anhand des vorgeschlagenen Modells implementieren wir einen konkreten Wide Baseline Stereo Matching Algorithmus, der besser mit schwacher Textur umgehen kann als herkömmliche Verfahren. Um die praktische Relevanz zu verdeutlichen, wenden wir den Algorithmus für die automatische Bildorientierung an. Hier besteht die Aufgabe darin, zu einer Menge überlappender Bilder die relativen 3D Kamerapositionen und Kameraorientierungen zu bestimmen. Wir zeigen, dass der Algorithmus im Fall schwach texturierter Szenen bessere Ergebnisse als etablierte Verfahren ermöglicht, und dennoch bei Standard-Datensätzen vergleichbare Ergebnisse liefert