This paper presents a deep convolutional neural field (DCNF) model for depth estimation from a single image. The model combines the strengths of deep convolutional neural networks (CNNs) and continuous conditional random fields (CRFs) in a unified framework. The authors formulate the depth estimation problem as a continuous CRF learning problem, leveraging the continuous nature of depth values. The partition function in the probability density function can be analytically calculated, allowing for exact log-likelihood optimization without approximations. The method jointly learns unary and pairwise potentials in a deep CNN framework, achieving translation invariance and explicit modeling of neighboring superpixel relationships. Experimental results on the NYU v2 and Make3D datasets demonstrate that the proposed method outperforms state-of-the-art depth estimation methods, both in terms of accuracy and efficiency. The model's performance is further validated through qualitative comparisons, showing sharper transitions and better alignment with local details.This paper presents a deep convolutional neural field (DCNF) model for depth estimation from a single image. The model combines the strengths of deep convolutional neural networks (CNNs) and continuous conditional random fields (CRFs) in a unified framework. The authors formulate the depth estimation problem as a continuous CRF learning problem, leveraging the continuous nature of depth values. The partition function in the probability density function can be analytically calculated, allowing for exact log-likelihood optimization without approximations. The method jointly learns unary and pairwise potentials in a deep CNN framework, achieving translation invariance and explicit modeling of neighboring superpixel relationships. Experimental results on the NYU v2 and Make3D datasets demonstrate that the proposed method outperforms state-of-the-art depth estimation methods, both in terms of accuracy and efficiency. The model's performance is further validated through qualitative comparisons, showing sharper transitions and better alignment with local details.