Disparity estimation method for weakly supervised trusted cost propagation

A binocular disparity estimation method based on weakly supervised trusted cost propagation, comprising the following steps:

• step 1, obtaining a sparse and accurate initial cost diagram by the traditional feature matching method, i.e., a non-deep learning method;
• step 2, conducting energy propagation;
• and optimizing the initial cost diagram by a three-dimensional convolutional network;
• step 3, conducting disparity regression;
• converting the optimized initial cost diagram into a probability diagram which is a probability that each pixel belongs to each disparity;
• then, obtaining a subpixel disparity through Soft Argmax;
• and finally obtaining a dense disparity diagram.