Current self-supervised monocular methods only learn effectively by imposing consistency constraints without relying on any geometric constraints or ground truth depth constraints, which makes the accuracy of the estimation result suboptimal. Compared with the monocular algorithm, the stereo matching network usually follows the geometric process of the traditional stereo algorithm, which makes the estimation result more accurate. Inspired by these findings, we proposed a weakly supervised monocular learning approach that makes use of the disparity maps generated by the self-supervised stereo matching model as the “ground truth” labels to train a self-supervised monocular depth estimation model. To obtain more accurate ground truth labels, we improve the layer of geometry and context in self-supervised deep stereo regression by replacing the 3D convolutional layer with a guided aggregation layer. The design can also reduce computational costs and memory consumption. Then, we build our weakly supervised monocular model by improving the U-Net model and designing a loss function composed of a weakly supervised cost and a self-supervised cost. The estimation results obtained using our model outperform those of the existing self-supervised depth estimation methods under the same training conditions on the challenging KITTI dataset, and the results can easily be generalized to the Cityscapes dataset.
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