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.