A novel cell structure‐based disparity estimation for unsupervised stereo matching

Abstract

Abstract It is well known that preserving depth edges is an effective solution for achieving the accurate disparity map in stereo matching, but many state‐of‐the‐art methods do not preserve depth edges well. In order to solve it efficiently, the cell structure containing irregular and regular shape regions is designed to preserve depth edges. Based on the well‐designed cell structure, a novel disparity estimation method for stereo matching is proposed, in which a two‐layer disparity optimization method is proposed to refine the disparity plane; it includes the front‐parallel disparities computation and slanted‐surfaces disparity plane refinement. In the framework of front‐parallel disparities computation, a tree‐based cost aggregation method is presented to make full use of the segmentation information of cells and then performing semi‐global cost aggregation. In the framework of slanted‐surfaces disparity plane refinement, a new probability model is proposed that employs Bayesian inference for refining disparities in textureless, weak texture and occluded regions. Experimental results show that higher accuracy could be achieved via the proposed method compared with some known state‐of‐the‐art stereo methods on KITTI 2015 and Middlebury dataset, which are the standard benchmarks for testing the stereo matching methods. It can also be indicated that the proposed method can produce accurate disparity map and have good generalization performance.