  In the field of remote sensing, the scarcity of stereo-matched and
particularly lack of accurate ground truth data often hinders the training of
deep neural networks. The use of synthetically generated images as an
alternative, alleviates this problem but suffers from the problem of domain
generalization. Unifying the capabilities of image-to-image translation and
stereo-matching presents an effective solution to address the issue of domain
generalization. Current methods involve combining two networks, an unpaired
image-to-image translation network and a stereo-matching network, while jointly
optimizing them. We propose an edge-aware GAN-based network that effectively
tackles both tasks simultaneously. We obtain edge maps of input images from the
Sobel operator and use it as an additional input to the encoder in the
generator to enforce geometric consistency during translation. We additionally
include a warping loss calculated from the translated images to maintain the
stereo consistency. We demonstrate that our model produces qualitatively and
quantitatively superior results than existing models, and its applicability
extends to diverse domains, including autonomous driving.
