  Inspired by the success of adversarial learning, we propose a new end-to-end
unsupervised deep learning framework for monocular depth estimation consisting
of two Generative Adversarial Networks (GAN), deeply coupled with a structured
Conditional Random Field (CRF) model. The two GANs aim at generating distinct
and complementary disparity maps and at improving the generation quality via
exploiting the adversarial learning strategy. The deep CRF coupling model is
proposed to fuse the generative and discriminative outputs from the dual GAN
nets. As such, the model implicitly constructs mutual constraints on the two
network branches and between the generator and discriminator. This facilitates
the optimization of the whole network for better disparity generation.
Extensive experiments on the KITTI, Cityscapes, and Make3D datasets clearly
demonstrate the effectiveness of the proposed approach and show superior
performance compared to state of the art methods. The code and models are
available at https://github.com/mihaipuscas/ 3dv---coupled-crf-disparity.
