The invention discloses a binocular
deep learning method based on adaptive single-peak
stereo matching cost filtering. The method is characterized in that single-peak distribution supervision with real
parallax as the center is directly applied to matching cost of network prediction, self-
adaptive matching cost filtering is achieved. The method comprises the following steps that (1) a
data set isconstructed, the
data set comprises a left image and a right image, and the left image and the right image serve as a three-dimensional
image pair; 2) taking the PSMNet as a
stereo matching model basic network, inputting the
stereo image pair into the PSMNet
stereo matching model basic network, and outputting three matching cost bodies (Cost Volume) aggregated by the stacked
hourglass 3D
convolutional neural network by the PSMNet stereo matching model basic network; (3) for each matching cost body (Cost Volume), calculating the matching cost of each matching cost body (Cost Volume); the methodcomprises the following steps of: estimating a self-confidence degree graph by using a self-confidence degree evaluation network (Confidence
Estimation Network) respectively, and adjusting a real matching cost volume (Group Truth Cost Volume) by using the self-confidence degree graph; the method comprises the following steps of: generating
unimodal distribution of a pixel level to serve as a network training mark; the device has the advantages that the defects in the prior art can be overcome, and the structural design is reasonable and novel.