The invention discloses a depth map recovery method, comprising the following steps of A1, constituting a training set by the depth maps of a large number of various objects; A2, establishing a convolutional neural network (CNN), by using a nuclear separation method, acquiring the parameter of a hidden layer, establishing a convolutional network structure, and training the network structure and adjusting the network weight by using the depth maps in the training set; A3, in the output layer of the CNN, establishing an auto-regression model aiming at a possible result, and establishing an evaluation index; and A4, inputting an original depth map acquired by a depth sensor into the CNN, after denoising and classifying, recovering by an AR model, and if not conforming with requirements, inputting the result map into A2 until the high-quality depth map is acquired or the circulation is ended. According to the depth map recovery method, the image with low resolution and low signal to noise ratio acquired from the depth sensor can be recovered by using the depth convolution network. By using the depth map recovery method, the quality of the depth map can be significantly improved, and meanwhile the method for acquiring the depth map is also simplified.