This paper addresses the challenging problem of depth estimation from single monocular images, which is more difficult compared to depth estimation using multiple images such as stereo depth perception. The authors propose a deep convolutional neural field (DCNF) model that jointly explores the capabilities of deep convolutional neural networks (CNNs) and continuous conditional random fields (CRFs). The DCNF model learns the unary and pairwise potentials of the CRF in a unified deep CNN framework, aiming to estimate depths from single monocular images. To improve efficiency, the authors also introduce a novel superpixel pooling method based on fully convolutional networks, which reduces the computational burden by about 10 times while maintaining similar prediction accuracy. The proposed method is evaluated on both indoor and outdoor scene datasets, demonstrating superior performance compared to state-of-the-art depth estimation approaches. The main contributions of the work include: 1. **Deep Convolutional Neural Field (DCNF) Model**: Formulates depth estimation as a continuous CRF learning problem, leveraging the continuous nature of depth values to directly solve the log-likelihood optimization without approximations. 2. **Joint Learning of Unary and Pairwise Potentials**: Learns the unary and pairwise potentials of the CRF in a unified deep CNN framework, improving depth estimation accuracy. 3. **Efficient Superpixel Pooling Method**: Introduces a novel superpixel pooling method based on fully convolutional networks, significantly reducing computational and memory costs while maintaining prediction accuracy. 4. **Performance on Datasets**: Demonstrates superior performance on both indoor (NYU v2) and outdoor (Make3D) datasets, outperforming state-of-the-art methods. The paper also provides a detailed description of the model architecture, implementation details, and experimental results, highlighting the effectiveness and efficiency of the proposed approach.This paper addresses the challenging problem of depth estimation from single monocular images, which is more difficult compared to depth estimation using multiple images such as stereo depth perception. The authors propose a deep convolutional neural field (DCNF) model that jointly explores the capabilities of deep convolutional neural networks (CNNs) and continuous conditional random fields (CRFs). The DCNF model learns the unary and pairwise potentials of the CRF in a unified deep CNN framework, aiming to estimate depths from single monocular images. To improve efficiency, the authors also introduce a novel superpixel pooling method based on fully convolutional networks, which reduces the computational burden by about 10 times while maintaining similar prediction accuracy. The proposed method is evaluated on both indoor and outdoor scene datasets, demonstrating superior performance compared to state-of-the-art depth estimation approaches. The main contributions of the work include: 1. **Deep Convolutional Neural Field (DCNF) Model**: Formulates depth estimation as a continuous CRF learning problem, leveraging the continuous nature of depth values to directly solve the log-likelihood optimization without approximations. 2. **Joint Learning of Unary and Pairwise Potentials**: Learns the unary and pairwise potentials of the CRF in a unified deep CNN framework, improving depth estimation accuracy. 3. **Efficient Superpixel Pooling Method**: Introduces a novel superpixel pooling method based on fully convolutional networks, significantly reducing computational and memory costs while maintaining prediction accuracy. 4. **Performance on Datasets**: Demonstrates superior performance on both indoor (NYU v2) and outdoor (Make3D) datasets, outperforming state-of-the-art methods. The paper also provides a detailed description of the model architecture, implementation details, and experimental results, highlighting the effectiveness and efficiency of the proposed approach.