Depth estimation with convolutional conditional random field network

In this paper, we tackle the problem of depth estimation from a single image, which is essential for understanding 3D scene structure and can promote the development of visual applications. Markov Random Field (MRF) related depth estimation methods have attracted extensive attentions due to their ability of building structural relationships on outputs. Almost all of them employed engineered low-level absolute and relative features for constructing MRF. However, the engineered features are not capable of producing accurate depths for various scenes. In this paper, we propose Convolutional Conditional Random Field Network (CCRFN) consisting of feature learning and depth tuning components. In feature learning component, we build two Convolutional Neural Network (CNN) architectures to learn absolute and relative features from raw images. In depth tuning component, the learned features are fed into Conditional Random Field (CRF) to generate the depths of all pixels in an image by optimizing a well-defined energy function. CCRFN has two advantages that (1) it does not need hand-crafted features and (2) it models the depths at individual points as well as the relationship between them in the deep model. Experiments on widely used Make3D dataset show that CCRFN outperforms state-of-the-art methods on three evaluation criteria and generates more accurate depth maps with sharper object boundaries.