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

Retexturing in the Presence of Complex Illumination and Occlusions

Figure 1: Adding a virtual character into a postcard. (a) The original image is partially hidden by a finger, which casts a shadow on it. (b) The picture of a woman has been added. It is correctly shaded and her feet are not shown since they would have been hidden by the finger, had she appeared in the original postcard. (c) What the postcard would have looked like if its middle part had been white. This can be viewed as diminished reality. Note that the shadow cast by the finger is correctly modeled. This figure, as well as most of the others, is best viewed in color. We present a non-rigid registration technique that achieves spatial, photometric, and visibility accuracy. It lets us photo-realistically augment 3D deformable surfaces under complex illumination conditions and in spite of severe occlusions. There are many approaches that address some of these issues but very few that simultaneously handle all of them as we do. We use triangulated meshes to model the geometry and introduce explicit visibility maps as well as separate illumination parameters for each mesh vertex. We cast our registration problem in an Expectation Maximization framework that allows robust and fully automated operation. It provides explicit illumination and occlusion models that can be used for rendering purposes