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In this work, we propose a new approach for dense disparity estimation in a global energy minimization framework. We propose to use a feature matching cost which is defined using the learned hierarchical features of given left and right stereo images and we combine it with the pixel-based intensity matching cost in our energy function. Hierarchical features are learned using the deep deconvolutional network...
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...
In this work, we propose to use an Inhomogeneous Gaussian Markov Random Field (IGMRF) and sparsity based priors in a regularization framework in order to estimate the dense disparity map. The IGMRF prior captures the smoothness as well as preserves sharp discontinuities and the sparsity prior captures the sparseness in the disparity map. We present a sparse autoencoder based approach for learning...
In this work, we propose to use an Inhomogeneous Gaussian Markov Random Field (IGMRF) and sparsity based priors in a regularization framework in order to estimate the dense disparity map. The IGMRF prior captures the spatial variation among disparities locally as well as it preserves sharp discontinuities. The sparsity prior captures the additional structure such as sparseness in the disparity map...
In this paper, we propose a learning based approach for solving the problem of dense stereo matching problem using edge preserving regularization prior. Given the test stereo pair and a training database consisting of disparity maps estimated using multiple views stereo images and their corresponding ground truths, we obtain the disparity map for the test set. We first obtain an initial disparity...
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