Dense Disparity Maps in Real-Time with an Application to Augmented Reality

This work presents a technique for computing dense disparity maps from a binocular stereo camera system. The methods are applied in an Augmented Reality setting for combining real and virtual worlds with proper occlusions. The proposed stereo correspondence technique is based on area matching and facilitates an efficient strategy by using the concept of a three-dimensional similarity accumulator, whereby occlusions are detected and object boundaries are extracted correctly. The main contribution of this paper is the way we fill the accumulator using absolute differences of images and computing a mean filter on these difference images. This is where the main advantages of the accumulator approach can be exploited, since all entries can be computed in parallel and thus extremely efficient. Additionally, we perform an asymmetric correction step and a post-processing of the disparity maps that maintains object edges.