This paper proposes a deep convolutional neural field model for depth estimation from a single image. The method combines the strengths of deep CNNs and continuous CRF to jointly learn unary and pairwise potentials. The model formulates depth estimation as a continuous CRF learning problem, allowing for exact optimization without approximations. The key contributions include: (1) a deep CNN-based model that directly solves the log-likelihood optimization using analytical calculations of the partition function; (2) a unified framework for learning unary and pairwise potentials; and (3) superior performance on both indoor and outdoor scene datasets compared to state-of-the-art methods. The model uses superpixels to represent depth values and incorporates pairwise similarities to enforce smoothness. It outperforms methods that rely on hand-crafted features or geometric priors. The model is efficient for depth prediction, with closed-form solutions for MAP inference. Experiments on the NYU v2 and Make3D datasets show that the proposed method achieves state-of-the-art results in depth estimation. The model is flexible and can be applied to other vision tasks, such as image denoising. The method is trained using back propagation and achieves high accuracy with minimal computational cost.This paper proposes a deep convolutional neural field model for depth estimation from a single image. The method combines the strengths of deep CNNs and continuous CRF to jointly learn unary and pairwise potentials. The model formulates depth estimation as a continuous CRF learning problem, allowing for exact optimization without approximations. The key contributions include: (1) a deep CNN-based model that directly solves the log-likelihood optimization using analytical calculations of the partition function; (2) a unified framework for learning unary and pairwise potentials; and (3) superior performance on both indoor and outdoor scene datasets compared to state-of-the-art methods. The model uses superpixels to represent depth values and incorporates pairwise similarities to enforce smoothness. It outperforms methods that rely on hand-crafted features or geometric priors. The model is efficient for depth prediction, with closed-form solutions for MAP inference. Experiments on the NYU v2 and Make3D datasets show that the proposed method achieves state-of-the-art results in depth estimation. The model is flexible and can be applied to other vision tasks, such as image denoising. The method is trained using back propagation and achieves high accuracy with minimal computational cost.