Dense Disparity Estimation Based on Feature Matching and IGMRF Regularization

In this paper, we propose a new approach for dense disparity estimation in a global energy minimization framework. We combine the feature matching cost defined using the learned hierarchical features of given left and right stereo images, with the pixel-based intensity matching cost to form the data term. The features are learned in an unsupervised way using the deep deconvolutional network. Our regularization term consists of an inhomogeneous Gaussian markov random field (IGMRF) prior that captures the smoothness as well as preserves sharp discontinuities in the disparity map. An iterative two phase algorithm is proposed to minimize the energy function in order to estimate the dense disparity map. In phase one, IGMRF parameters are computed, keeping the disparity map fixed, and in phase two, the disparity map is refined by minimizing the energy function using graph cuts, with other parameters fixed. Experimental results on the Middlebury stereo benchmarks demonstrate the effectiveness of the proposed approach.