Confidence evaluation for robust, fast-converging disparity map refinement

To improve upon the initial disparity estimates stemming from a local correspondence method, a subsequent refinement step is commonly employed. The performance of the stereo matcher used for the initial estimation inherently depends on the underlying image content. For some regions of an image, it may be difficult or even physically impossible to establish accurate point correspondences. This results in disparity estimates of varying accuracy and reliability. In this paper, a confidence map is proposed which combines the consistency and quality of a match. It explicitly models the reliability of each disparity estimate. Such a confidence map represents valuable, additional information that can be leveraged in subsequent steps of the 3D processing chain. In this regard, this paper presents an extension of a cross bilateral filter that leverages this reliability information during a fast-converging refinement step in order to create robust and reliable disparity maps.