  Unsupervised depth learning takes the appearance difference between a target
view and a view synthesized from its adjacent frame as supervisory signal.
Since the supervisory signal only comes from images themselves, the resolution
of training data significantly impacts the performance. High-resolution images
contain more fine-grained details and provide more accurate supervisory signal.
However, due to the limitation of memory and computation power, the original
images are typically down-sampled during training, which suffers heavy loss of
details and disparity accuracy. In order to fully explore the information
contained in high-resolution data, we propose a simple yet effective dual
networks architecture, which can directly take high-resolution images as input
and generate high-resolution and high-accuracy depth map efficiently. We also
propose a Self-assembled Attention (SA-Attention) module to handle low-texture
region. The evaluation on the benchmark KITTI and Make3D datasets demonstrates
that our method achieves state-of-the-art results in the monocular depth
estimation task.
