Mixing video and computer-generated images is a new and promising area of research for enhancing reality. It can be used in all the situations when a complete simulation would not be easy to implement. Past work on the subject has relied for a large part on human intervention at key moments of the composition. In this paper, we show that if enough geometric information about the environment is available, then efficient tools developed in the computer vision literature can be used to build a highly automated augmented reality loop. We focus on outdoor urban environments and present an application for the visual assessment of a new lighting project of the bridges of Paris. We present a fully augmented 300-image sequence of a specific bridge, the Pont Neuf. Emphasis is put on the robust calculation of the camera position. We also detail the techniques used for matching 2D and 3D primitives and for tracking features over the sequence. Our system overcomes two major difficulties. First, it is capable of handling poor-quality images, resulting from the fact that images were shot at night since the goal was to simulate a new lighting system. Second, it can deal with important changes in viewpoint position and in appearance along the sequence. Throughout the paper, many results are shown to illustrate the different steps and difficulties encountered.
['Marie-Odile Berger', 'Brigitte Wrobel-Dautcourt', 'Sylvain Petitjean', 'Gilles Simon']
Mixing synthetic and video images of an outdoor urban environment