Efficient depth map estimation method based on gradient weight cost aggregation strategy

A cross-based framework strategy for performance of gradient-based weight cost aggregation strategy is presented. We formulate the process as a local regression problem consisting of two main steps. The first step is to calculate estimates for a set of points within a shape-adaptive local support region. The second step is to aggregate the matching cost for the gradient-based weight of the support region at the outmost pixel. The proposed algorithm achieves strong results in an efficient manner using the two main steps. We have achieved improvement of up to 6.9%, 8.4% and 8.3%, when compared with Adaptive Support weight (ASW) algorithm. Comparing to Cross-based algorithm, the proposed algorithm gives 2.0%, 1.3% and 1.0% in terms of non-occlusion, all and discontinuities, respectively.