Structured Deep Learning Based Depth Estimation from a Monocular Image

For the purposes of extracting rich 3D structural features from a monocular image and inferring depth information for the scene, a structured deep learning model is proposed for the task of depth estimation from a monocular image. The model combines a novel multi-scale convolutional neural network (CNN) and continuous conditional random field (CCRF) in a unified deep learning framework. CNN can learn related feature representations from an image, and CCRF can optimize the output of CNN according to the position and color information of the image pixels. By jointly learning the parameters of CCRF and CNN, the generalization ability of the model can be improved. Experiments on NYU Depth dataset demonstrate the effectiveness and superiority of the model. The average relative error of the predictions of the model is 0.187, and the root mean squared error is 0.074, the average log₁₀ error is 0.671.