  Recent techniques in self-supervised monocular depth estimation are
approaching the performance of supervised methods, but operate in low
resolution only. We show that high resolution is key towards high-fidelity
self-supervised monocular depth prediction. Inspired by recent deep learning
methods for Single-Image Super-Resolution, we propose a sub-pixel convolutional
layer extension for depth super-resolution that accurately synthesizes
high-resolution disparities from their corresponding low-resolution
convolutional features. In addition, we introduce a differentiable
flip-augmentation layer that accurately fuses predictions from the image and
its horizontally flipped version, reducing the effect of left and right shadow
regions generated in the disparity map due to occlusions. Both contributions
provide significant performance gains over the state-of-the-art in
self-supervised depth and pose estimation on the public KITTI benchmark. A
video of our approach can be found at https://youtu.be/jKNgBeBMx0I.
