Dense-disparity estimation from feature correspondences

Abstract

Stereoscopic disparity plays an important role in the processing and compression of 3D imagery. For example, dense disparity fields are used to reconstruct intermediate images. Although for small camera baselines dense disparity can be reliably estimated using gradient-based methods, this is not the case for large baselines due to the violation of underlying assumptions. Block matching algorithms work better but they are likely to get trapped in a local minimum due to the increased search space. An appropriate method to estimate large disparities is by using feature points. However, since feature points are unique, they are also sparse. In this paper, we propose a disparity estimation method that combines the reliability of feature-based correspondence methods with the resolution of dense approaches. In the first step we find feature points in the left and right images using Harris operator. In the second step, we select those feature points that allow one-to-one left-right correspondence based on a cross-correlation measure. In the third step, we use the computed correspondence points to control the computation of dense disparity via regularized block matching that minimizes matching and disparity smoothness errors. The approach has been tested on several large-baseline stereo pairs with encouraging initial results.