Edge-preserving efficient dense stereo matching

Edge accurate dense disparity estimation is of great importance to applications, such as augmented reality where the geometry relationships among objects in a scene need to be presented precisely. Binocular stereo is a promising approach to get 3D depth information of a real scene from 2D images, but faces the issue of difficult to achieve high accurate disparity edges with reasonable computation complexity. A depth edge preserving dense stereo matching method is presented in this paper aiming to alleviate this problem. By taking a sparse-to-dense route for disparity estimation, depth edges corresponding to object boundaries are distinguished from the texture edges based on sparse disparities which can be obtained efficiently. With a designed disparity filling strategy, these extracted edges are used to refine the dense disparities and align the depth discontinuity edges with corresponding object boundaries. Disparities obtained by this work can faithfully conform to the scene geometry recorded in the input images only with a relative small increase about 13% in computational complexity. The effectiveness of the proposed method is verified through experiments and contrastive analysis.