Dynamic update of dense depth map by Kalman filtering

Recovering three-dimensional visible surfaces is an important cue in computer vision. Like most of the inverse problems it is hard to be solved due to the insufficient and inaccurate data that can be collected using passive sensors. Data fusion and integration over time can be used to overcome this problem. In this paper the depth and orientation are acquired from a scene and are used together to build a dense map of the visible surface for each of several points of view. An incremental estimator is used to integrate these maps, as soon as they become available, in a more reliable result.<>