Fast Non-Rigid Surface Detection, Registration and Realistic Augmentation

We present a real-time method for detecting deformable surfaces, with no need whatsoever for a priori pose knowledge. Our method starts from a set of wide baseline point matches between an undeformed image of the object and the image in which it is to be detected. The matches are used not only to detect but also to compute a precise mapping from one to the other. The algorithm is robust to large deformations, lighting changes, motion blur, and occlusions. It runs at 10 frames per second on a 2.8 GHz PC.We demonstrate its applicability by using it to realistically modify the texture of a deforming surface and to handle complex illumination effects. Combining deformable meshes with a well designed robust estimator is key to dealing with the large number of parameters involved in modeling deformable surfaces and rejecting erroneous matches for error rates of more than 90%, which is considerably more than what is required in practic

Augmented reality for non-rigid surfaces

Augmented Reality (AR) is the process of integrating virtual elements in reality, often by mixing computer graphics into a live video stream of a real scene. It requires registration of the target object with respect to the cameras. To this end, some approaches rely on dedicated hardware, such as magnetic trackers or infra-red cameras, but they are too expensive and cumbersome to reach a large public. Others are based on specifically designed markers which usually look like bar-codes. However, they alter the look of objects to be augmented, thereby hindering their use in application for which visual design matters. Recent advances in Computer Vision have made it possible to track and detect objects by relying on natural features. However, no such method is commonly used in the AR community, because the maturity of available packages is not sufficient yet. As far as deformable surfaces are concerned, the choice is even more limited, mainly because initialization is so difficult. Our main contribution is therefore a new AR framework that can properly augment deforming surfaces in real-time. Its target platform is a standard PC and a single webcam. It does not require any complex calibration procedure, making it perfectly suitable for novice end-users. To satisfy to the most demanding application designers, our framework does not require any scene engineering, renders virtual objects illuminated by real light, and let real elements occlude virtual ones. To meet this challenge, we developed several innovative techniques. Our approach to real-time registration of a deforming surface is based on wide-baseline feature matching. However, traditional outlier elimination techniques such as RANSAC are unable to handle the non-rigid surface's large number of degrees of freedom. We therefore proposed a new robust estimation scheme that allows both 2–D and 3–D non-rigid surface registration. Another issue of critical importance in AR to achieve realism is illumination handling, for which existing techniques often require setup procedures or devices such as reflective spheres. By contrast, our framework includes methods to estimate illumination for rendering purposes without sacrificing ease of use. Finally, several existing approaches to handling occlusions in AR rely on multiple cameras or can only deal with occluding objects modeled beforehand. Our requires only one camera and models occluding objects at runtime. We incorporated these components in a consistent and flexible framework. We used it to augment many different objects such as a deforming T-shirt or a sheet of paper, under challenging conditions, in real-time, and with correct handling of illumination and occlusions. We also used our non-rigid surface registration technique to measure the shape of deformed sails. We validated the ease of deployment of our framework by distributing a software package and letting an artist use it to create two AR applications

Fast Non-Rigid Surface Detection, Registration and Realistic Augmentation

We present a real-time method for detecting deformable surfaces, with no need whatsoever for a priori pose knowledge. Our method starts from a set of wide baseline point matches between an undeformed image of the object and the image in which it is to be detected. The matches are used not only to detect but also to compute a precise mapping from one to the other. The algorithm is robust to large deformations, lighting changes, motion blur, and occlusions. It runs at 10 frames per second on a 2.8 GHz PC.We demonstrate its applicability by using it to realistically modify the texture of a deforming surface and to handle complex illumination effects. Combining deformable meshes with a well designed robust estimator is key to dealing with the large number of parameters involved in modeling deformable surfaces and rejecting erroneous matches for error rates of more than 90%, which is considerably more than what is required in practice

Augmenting Deformable Objects in Real-Time

We present a real-time system that can draw virtual patterns or images on deforming real objects by estimating both the deformations and the shading parameters. We show that this is what is required to render the virtual elements so that they blend convincingly with the surrounding real textures. The whole process of uncompressing the video stream, measuring the deformations, estimating the lighting parameters, and realistically augmenting the input image takes about 100 ms on a 2.8 GHz PC. It is fully automated and does not require any manual initialization or engineering of the scene. It is also robust to large deformations, lighting changes, motion blur, specularities, and occlusions. It can therefore be demonstrated live on a simple laptop

Making Background Subtraction Robust to Sudden Illumination Changes

Modern background subtraction techniques can handle gradual illumination changes but can easily be confused by rapid ones. We propose a technique that overcomes this limitation by relying on a statistical model, not of the pixel intensities, but of the illumination effects. Because they tend to affect whole areas of the image as opposed to individual pixels, low-dimensional models are appropriate for this purpose and make our method extremely robust to illumination changes, whether slow or fast

Real-Time Non-Rigid Surface Detection

We present a real-time method for detecting deformable surfaces, with no need whatsoever for a priori pose knowledge. Our method starts from a set of wide baseline point matches between an undeformed image of the object and the image in which it is to be detected. The matches are used not only to detect but also to compute a precise mapping from one to the other. The algorithm is robust to large deformations, lighting changes, motion blur, and occlusions. It runs at 10 frames per second on a 2.8 GHz PC and we are not aware of any other published technique that produces similar results. Combining deformable meshes with a well designed robust estimator is key to dealing with the large number of parameters involved in modeling deformable surfaces and rejecting erroneous matches for error rates of up to 95%, which is considerably more than what is required in practice

Surface Deformation Models for Non-Rigid 3--D Shape Recovery

3--D detection and shape recovery of a non-rigid surface from video sequences require deformation models to effectively take advantage of potentially noisy image data. Here we introduce an approach to creating such models for deformable 3--D surfaces. We exploit the fact that the shape of an inextensible triangulated mesh can be parameterized in terms of a small subset of the angles between its facets. We use this set of angles to create a representative set of potential shapes, which we feed to a simple dimensionality reduction technique to produce low-dimensional 3--D deformation models. We show that these models can be used to accurately model a wide range of deforming 3--D surfaces from video sequences acquired under realistic conditions

Memories of the world of the mountains

This paper describes a particular book called Souvenirs du monde des montagnes, which draws its iconography from the history of a Swiss mountain family from 1910 to 1930. By simply dipping into the first few pages, the reader will be lost between real and virtual universes, wonder about the evolution of the images' meanings, and question an object's true content. This setup, developed using state-of-the-art computer vision technology, offers unprecedented freedom: we can make technological references disappear to place the user in fruitful turmoil between visible and hidden meanings. The shadow of a bird flies over the pages, foxes' lanterns light up the text, paper mountains emerge. Once the last page has been turned, the reader will never look at books in the same way again

An all-in-one solution to geometric and photometric calibration

We propose a fully automated approach to calibrating multiple cameras whose fields of view may not all overlap. Our technique only requires waving an arbitrary textured planar pattern in front of the cameras, which is the only manual intervention that is required. The pattern is then automatically detected in the frames where it is visible and used to simultaneously recover geometric and photometric camera calibration parameters. In other words, even a novice user can use our system to extract all the information required to add virtual 3D objects into the scene and light them convincingly. This makes it ideal for Augmented Reality applications and we distribute the code under a GPL license

Engine steady-state analysis in windmilling operation coupling thermodynamic modeling and CFD simulations

L'étude des performances moteur repose traditionnellement sur des modèles de cycle thermodynamique et l'utilisation de champs caractéristiques pour décrire le comportement de sous ensembles élémentaires (compresseurs, turbines,...). Ces modèles simplifiés permettent de prendre en compte les équilibres et interactions entre les différents composants de la turbomachine et ses effets technologiques, En pratique, ces caractéristiques sont issues de techniques d'interpolation/ extrapolation (par exemple la méthode MFT) de d01hnées d'essais ou de calculs aérodynamiques (1D, 2D ou 3D), le plus souvent disponibles seulement autour du point de fonctionnement nominal. Par conséquent, la représentativité de ces caractéristiques n'est pas toujours satisfaisante pour simuler des points de fonctionnement en forte hors adaptation, comme les ralentis ou le windmilling. A l'inverse, les outils de calcul aérodynamique 3D sont capables de simuler des écoulements plus complexes pour tout point de fonctionnement (proche du nominal), Toutefois, leur utilisation est en pratique limitée aux différents sous-ensembles pris séparément, en raison des temps de restitution particulièrement longs pour la simulation d'un moteur complet. Par conséquent, les interactions entre composants ne sont pas prises en compte, d'où la difficulté de prévoir les performances du système propulsif dans son intégralité. L'objectif de ce travail est d'une part de combler ce besoin d'outils de prévision fiables des performances moteur pour des fonctionnements en très forte hors-adaptation et, d'autre part, d'analyser la phénoménologie des écoulements en windmilling.Engine performance is traditionally calculated by thermodynamic models (engine cycle analysis) using characteristic maps to describe engine sub-components behavior (compressors, turbines,...). These simplified models can account for the equilibriums and interactions between all the sub components of the engine, as well as the different technological effects. Interpolation and extrapolation techniques such as th MFT(Map Fitting Tool) are used to build up the characteristic maps with data collected from aerodynamic calculations (CFD, 1D, 2D or 3D) or rig tests that are usually available at design point. However, such techniques do not always provide the level of accuracy needed for off-design cycle analysis such as low speeds and windmilling operatlon. ln addition, these maps do not provide any insight on the physical phenomena governing thls kind of operations.Aerodynamic calculatlon tools are able to simulate complex 3D flows for nearly any operating conditions with a fairly good accuracy. However, they are commonly used on individual sub-components and not the whole engine due to high computing time and resources they require. Therefore, interactions between sub-components are overlooked making it difficult to predict the overall engine performance.The objectives of this thesis are to improve severe off-design engine performance predictions and to understand the physical phenomena in place at steacly-state windmilling operation. Engine winclmilling performance ls critical in early design phase of the primary combustion chamber area that will cletermine engine relight capabilities. Yet, knowledge of how the engine operates during windmilling is still scarce