Current methods for single-image depth estimation use training datasets with
real image-depth pairs or stereo pairs, which are not easy to acquire. We
propose a framework, trained on synthetic image-depth pairs and unpaired real
images, that comprises an image translation network for enhancing realism of
input images, followed by a depth prediction network. A key idea is having the
first network act as a wide-spectrum input translator, taking in either
synthetic or real images, and ideally producing minimally modified realistic
images. This is done via a reconstruction loss when the training input is real,
and GAN loss when synthetic, removing the need for heuristic
self-regularization. The second network is trained on a task loss for synthetic
image-depth pairs, with extra GAN loss to unify real and synthetic feature
distributions. Importantly, the framework can be trained end-to-end, leading to
good results, even surpassing early deep-learning methods that use real paired
data.Comment: 15 pages, 8 figure
