Unsupervised Monocular Depth Estimation with Semantic Reconstruction Using Dual-Discriminator Generative Adversarial

Monocular depth estimation is a key issue in the field of computer vision. The unsupervised learning framework has the advantage of not requiring data labels, and has become a hot research topic. Currently, most methods use view synthesis as a supervisory signal, resulting in unclear edges and semantic distortion of predicted results in some situations. We proposed a new framework that introduce a semantic reconstruction loss to provide additional constraints for the network and improve the ability of the depth network to understand scenarios. In addition, we proposed a dual-discriminator adversarial training strategy to further strengthen semantic supervision and improve the accuracy of depth estimation. The test results show that our proposed method has achieved competitive performance on the KITTI dataset.