The invention discloses a multi-view depth acquisition method, relates to the field of
computer vision and the technical field of
deep learning, solves a
depth map by using a
machine learning mode, and has better robustness for shooting angle problems such as wide baselines and complex texture and shadow problems such as rough areas, weak texture areas and shielding. A CBAM attention mechanism is introduced into a
feature extraction module, and features obtained by
convolution each time are sorted in two directions of a channel dimension and a space dimension. Hopping layer connection in a
feature extraction Unet structure ensures that high-level information is not covered, and low-level information is obtained at the same time. The
feature extraction Unet and the CBAM attention mechanism fully consider the relation of
geometric mapping of different view angles, and the recognition capability of the feature extraction module on different
view angle features is improved. In a cost regularization part, a mode of combining 3D
convolution and bidirectional long short-
term memory (LSTM) is used, and the three-dimensional variance characteristics are regularized from two aspects of
depth dimension and channel dimension, so that the
network processing is improved, and the generation speed is high.