Monocular depth estimation plays a very important role in many applications such as 3D reconstruction, target tracking, and scene understanding. Since monocular cameras have the characteristics of low cost, widespread equipment, and convenient image acquisition, obtaining depth information from monocular images has become a hot research topic. First, the common deep learning models used for monocular depth estimation are summarized, mainly including convolutional neural network (CNN), recurrent neural network (RNN), and generative adversarial network (GAN). Then, the deep learning methods for monocular depth estimation are summarized from the perspective of training methods, and the development trend of monocular depth estimation is summarized.