Finite Element Based Tracking of Deforming Surfaces

We present an approach to robustly track the geometry of an object that deforms over time from a set of input point clouds captured from a single viewpoint. The deformations we consider are caused by applying forces to known locations on the object's surface. Our method combines the use of prior information on the geometry of the object modeled by a smooth template and the use of a linear finite element method to predict the deformation. This allows the accurate reconstruction of both the observed and the unobserved sides of the object. We present tracking results for noisy low-quality point clouds acquired by either a stereo camera or a depth camera, and simulations with point clouds corrupted by different error terms. We show that our method is also applicable to large non-linear deformations.Comment: additional experiment

Video normals from colored lights

We present an algorithm and the associated single-view capture methodology to acquire the detailed 3D shape, bends, and wrinkles of deforming surfaces. Moving 3D data has been difficult to obtain by methods that rely on known surface features, structured light, or silhouettes. Multispectral photometric stereo is an attractive alternative because it can recover a dense normal field from an untextured surface. We show how to capture such data, which in turn allows us to demonstrate the strengths and limitations of our simple frame-to-frame registration over time. Experiments were performed on monocular video sequences of untextured cloth and faces with and without white makeup. Subjects were filmed under spatially separated red, green, and blue lights. Our first finding is that the color photometric stereo setup is able to produce smoothly varying per-frame reconstructions with high detail. Second, when these 3D reconstructions are augmented with 2D tracking results, one can register both the surfaces and relax the homogenous-color restriction of the single-hue subject. Quantitative and qualitative experiments explore both the practicality and limitations of this simple multispectral capture system

Doctor of Philosophy

dissertationShape analysis is a well-established tool for processing surfaces. It is often a first step in performing tasks such as segmentation, symmetry detection, and finding correspondences between shapes. Shape analysis is traditionally employed on well-sampled surfaces where the geometry and topology is precisely known. When the form of the surface is that of a point cloud containing nonuniform sampling, noise, and incomplete measurements, traditional shape analysis methods perform poorly. Although one may first perform reconstruction on such a point cloud prior to performing shape analysis, if the geometry and topology is far from the true surface, then this can have an adverse impact on the subsequent analysis. Furthermore, for triangulated surfaces containing noise, thin sheets, and poorly shaped triangles, existing shape analysis methods can be highly unstable. This thesis explores methods of shape analysis applied directly to such defect-laden shapes. We first study the problem of surface reconstruction, in order to obtain a better understanding of the types of point clouds for which reconstruction methods contain difficulties. To this end, we have devised a benchmark for surface reconstruction, establishing a standard for measuring error in reconstruction. We then develop a new method for consistently orienting normals of such challenging point clouds by using a collection of harmonic functions, intrinsically defined on the point cloud. Next, we develop a new shape analysis tool which is tolerant to imperfections, by constructing distances directly on the point cloud defined as the likelihood of two points belonging to a mutually common medial ball, and apply this for segmentation and reconstruction. We extend this distance measure to define a diffusion process on the point cloud, tolerant to missing data, which is used for the purposes of matching incomplete shapes undergoing a nonrigid deformation. Lastly, we have developed an intrinsic method for multiresolution remeshing of a poor-quality triangulated surface via spectral bisection

A Survey of Surface Reconstruction from Point Clouds

International audienceThe area of surface reconstruction has seen substantial progress in the past two decades. The traditional problem addressed by surface reconstruction is to recover the digital representation of a physical shape that has been scanned, where the scanned data contains a wide variety of defects. While much of the earlier work has been focused on reconstructing a piece-wise smooth representation of the original shape, recent work has taken on more specialized priors to address significantly challenging data imperfections, where the reconstruction can take on different representations – not necessarily the explicit geometry. We survey the field of surface reconstruction, and provide a categorization with respect to priors, data imperfections, and reconstruction output. By considering a holistic view of surface reconstruction, we show a detailed characterization of the field, highlight similarities between diverse reconstruction techniques, and provide directions for future work in surface reconstruction

Modélisation 4D à partir de plusieurs caméras

Les systèmes multi-caméras permettent de nos jours d'obtenir à la fois des flux d'images couleur mais aussi des flux de modèles 3D. Ils permettent ainsi l'étude de scènes complexes à la fois de par les éléments qui la composent mais aussi de par les mouvements et les déformations que subissent ces éléments au fil du temps. Une des principales limitations de ces données est le manque de cohérence temporelle entre les observations obtenues à deux instants de temps successifs. Les travaux présentés dans cette thèse proposent des pistes pour retrouver cette cohérence temporelle. Dans un premier temps nous nous sommes penchés sur le problème de l'estimation de champs de déplacement denses à la surface des objets de la scène. L'approche que nous proposons permet de combiner efficacement des informations photométriques provenant des caméras avec des informations géométriques. Cette méthode a été étendue, par la suite, au cas de systèmes multi-caméras hybrides composés de capteurs couleurs et de profondeur (tel que le capteur kinect). Dans un second temps nous proposons une méthode nouvelle permettant l'apprentissage de la vraie topologie d'une scène dynamique au fil d'une séquence de données 4D (3D + temps). Ces travaux permettent de construire au fur et à mesure des observations un modèle de référence de plus en plus complet de la scène observée.Nowadays mutli-camera setups allow the acquisition of both color image streams and 3D models streams. Thus permitting the study of complex scenes. These scenes can be composed of any number of non-rigid objects moving freely. One of the main limitations of such data is its lack of temporal coherence between two consecutive observations. The work presented in this thesis consider this issue and propose novel methods to recover this temporal coherence. First we present a new approach that computes at each frame a dense motion field over the surface of the scene (i.e. Scene Flow), gathering both photometric and geometric information. We then extend this approach to hybrid multi-camera setups composed of color and depth sensor (such as the kinect sensor). Second, we introduce "Progressive Shape Models", a new method that allows to gather topology information over a complete sequence of 3D models and incrementally build a complete and coherent surface template.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Inverse rendering for scene reconstruction in general environments

Demand for high-quality 3D content has been exploding recently, owing to the advances in 3D displays and 3D printing. However, due to insufficient 3D content, the potential of 3D display and printing technology has not been realized to its full extent. Techniques for capturing the real world, which are able to generate 3D models from captured images or videos, are a hot research topic in computer graphics and computer vision. Despite significant progress, many methods are still highly constrained and require lots of prerequisites to succeed. Marker-less performance capture is one such dynamic scene reconstruction technique that is still confined to studio environments. The requirements involved, such as the need for a multi-view camera setup, specially engineered lighting or green-screen backgrounds, prevent these methods from being widely used by the film industry or even by ordinary consumers. In the area of scene reconstruction from images or videos, this thesis proposes new techniques that succeed in general environments, even using as few as two cameras. Contributions are made in terms of reducing the constraints of marker-less performance capture on lighting, background and the required number of cameras. The primary theoretical contribution lies in the investigation of light transport mechanisms for high-quality 3D reconstruction in general environments. Several steps are taken to approach the goal of scene reconstruction in general environments. At first, the concept of employing inverse rendering for scene reconstruction is demonstrated on static scenes, where a high-quality multi-view 3D reconstruction method under general unknown illumination is developed. Then, this concept is extended to dynamic scene reconstruction from multi-view video, where detailed 3D models of dynamic scenes can be captured under general and even varying lighting, and in front of a general scene background without a green screen. Finally, efforts are made to reduce the number of cameras employed. New performance capture methods using as few as two cameras are proposed to capture high-quality 3D geometry in general environments, even outdoors.Die Nachfrage nach qualitativ hochwertigen 3D Modellen ist in letzter Zeit, bedingt durch den technologischen Fortschritt bei 3D-Wieder-gabegeräten und -Druckern, stark angestiegen. Allerdings konnten diese Technologien wegen mangelnder Inhalte nicht ihr volles Potential entwickeln. Methoden zur Erfassung der realen Welt, welche 3D-Modelle aus Bildern oder Videos generieren, sind daher ein brandaktuelles Forschungsthema im Bereich Computergrafik und Bildverstehen. Trotz erheblichen Fortschritts in dieser Richtung sind viele Methoden noch stark eingeschränkt und benötigen viele Voraussetzungen um erfolgreich zu sein. Markerloses Performance Capturing ist ein solches Verfahren, das dynamische Szenen rekonstruiert, aber noch auf Studio-Umgebungen beschränkt ist. Die spezifischen Anforderung solcher Verfahren, wie zum Beispiel einen Mehrkameraaufbau, maßgeschneiderte, kontrollierte Beleuchtung oder Greenscreen-Hintergründe verhindern die Verbreitung dieser Verfahren in der Filmindustrie und besonders bei Endbenutzern. Im Bereich der Szenenrekonstruktion aus Bildern oder Videos schlägt diese Dissertation neue Methoden vor, welche in beliebigen Umgebungen und auch mit nur wenigen (zwei) Kameras funktionieren. Dazu werden Schritte unternommen, um die Einschränkungen bisheriger Verfahren des markerlosen Performance Capturings im Hinblick auf Beleuchtung, Hintergründe und die erforderliche Anzahl von Kameras zu verringern. Der wichtigste theoretische Beitrag liegt in der Untersuchung von Licht-Transportmechanismen für hochwertige 3D-Rekonstruktionen in beliebigen Umgebungen. Dabei werden mehrere Schritte unternommen, um das Ziel der Szenenrekonstruktion in beliebigen Umgebungen anzugehen. Zunächst wird die Anwendung von inversem Rendering auf die Rekonstruktion von statischen Szenen dargelegt, indem ein hochwertiges 3D-Rekonstruktionsverfahren aus Mehransichtsaufnahmen unter beliebiger, unbekannter Beleuchtung entwickelt wird. Dann wird dieses Konzept auf die dynamische Szenenrekonstruktion basierend auf Mehransichtsvideos erweitert, wobei detaillierte 3D-Modelle von dynamischen Szenen unter beliebiger und auch veränderlicher Beleuchtung vor einem allgemeinen Hintergrund ohne Greenscreen erfasst werden. Schließlich werden Anstrengungen unternommen die Anzahl der eingesetzten Kameras zu reduzieren. Dazu werden neue Verfahren des Performance Capturings, unter Verwendung von lediglich zwei Kameras vorgeschlagen, um hochwertige 3D-Geometrie im beliebigen Umgebungen, sowie im Freien, zu erfassen

Deformable shape matching

Deformable shape matching has become an important building block in academia as well as in industry. Given two three dimensional shapes A and B the deformation function f aligning A with B has to be found. The function is discretized by a set of corresponding point pairs. Unfortunately, the computation cost of a brute-force search of correspondences is exponential. Additionally, to be of any practical use the algorithm has to be able to deal with data coming directly from 3D scanner devices which suffers from acquisition problems like noise, holes as well as missing any information about topology. This dissertation presents novel solutions for solving shape matching: First, an algorithm estimating correspondences using a randomized search strategy is shown. Additionally, a planning step dramatically reducing the matching costs is incorporated. Using ideas of these both contributions, a method for matching multiple shapes at once is shown. The method facilitates the reconstruction of shape and motion from noisy data acquired with dynamic 3D scanners. Considering shape matching from another perspective a solution is shown using Markov Random Fields (MRF). Formulated as MRF, partial as well as full matches of a shape can be found. Here, belief propagation is utilized for inference computation in the MRF. Finally, an approach significantly reducing the space-time complexity of belief propagation for a wide spectrum of computer vision tasks is presented.Anpassung deformierbarer Formen ist zu einem wichtigen Baustein in der akademischen Welt sowie in der Industrie geworden. Gegeben zwei dreidimensionale Formen A und B, suchen wir nach einer Verformungsfunktion f, die die Deformation von A auf B abbildet. Die Funktion f wird durch eine Menge von korrespondierenden Punktepaaren diskretisiert. Leider sind die Berechnungskosten für eine Brute-Force-Suche dieser Korrespondenzen exponentiell. Um zusätzlich von einem praktischen Nutzen zu sein, muss der Suchalgorithmus in der Lage sein, mit Daten, die direkt aus 3D-Scanner kommen, umzugehen. Bedauerlicherweise leiden diese Daten unter Akquisitionsproblemen wie Rauschen, Löcher sowie fehlender Topologieinformation. In dieser Dissertation werden neue Lösungen für das Problem der Formanpassung präsentiert. Als erstes wird ein Algorithmus gezeigt, der die Korrespondenzen mittels einer randomisierten Suchstrategie schätzt. Zusätzlich wird anhand eines automatisch berechneten Schätzplanes die Geschwindigkeit der Suchstrategie verbessert. Danach wird ein Verfahren gezeigt, dass die Anpassung mehrerer Formen gleichzeitig bewerkstelligen kann. Diese Methode ermöglicht es, die Bewegung, sowie die eigentliche Struktur des Objektes aus verrauschten Daten, die mittels dynamischer 3D-Scanner aufgenommen wurden, zu rekonstruieren. Darauffolgend wird das Problem der Formanpassung aus einer anderen Perspektive betrachtet und als Markov-Netzwerk (MRF) reformuliert. Dieses ermöglicht es, die Formen auch stückweise aufeinander abzubilden. Die eigentliche Lösung wird mittels Belief Propagation berechnet. Schließlich wird ein Ansatz gezeigt, der die Speicher-Zeit-Komplexität von Belief Propagation für ein breites Spektrum von Computer-Vision Problemen erheblich reduziert

Globally consistent space-time reconstruction

We present a novel algorithm for space-time reconstruction of deforming meshes. Based on partial meshes at every frame, and sparse optical flow information between frames, we reconstruct a globally consistent, crossparameterized, and hole filled sequence of meshes. Our method is based on pair-wise merging of frame sequences while correcting for changes in topology, filling in missing geometry, and repairing inconsistencies. We also introduce a robust method for filling in missing geometry in each frame of the sequence using geometry from another frame. Using this method we can propagate geometry over the full frame sequence, correcting errors and filling in holes even in regions of the object that are not observed in the input meshes for extended periods of time. Unlike other approaches, our method does not require template geometry, nor is it limited to narrow classes of objects or purely isometric deformations.Science, Faculty ofComputer Science, Department ofGraduat