  Self-supervised learning shows great potential in monoculardepth estimation,
using image sequences as the only source ofsupervision. Although people try to
use the high-resolutionimage for depth estimation, the accuracy of prediction
hasnot been significantly improved. In this work, we find thecore reason comes
from the inaccurate depth estimation inlarge gradient regions, making the
bilinear interpolation er-ror gradually disappear as the resolution increases.
To obtainmore accurate depth estimation in large gradient regions, itis
necessary to obtain high-resolution features with spatialand semantic
information. Therefore, we present an improvedDepthNet, HR-Depth, with two
effective strategies: (1) re-design the skip-connection in DepthNet to get
better high-resolution features and (2) propose feature fusion
Squeeze-and-Excitation(fSE) module to fuse feature more efficiently.Using
Resnet-18 as the encoder, HR-Depth surpasses all pre-vious
state-of-the-art(SoTA) methods with the least param-eters at both high and low
resolution. Moreover, previousstate-of-the-art methods are based on fairly
complex and deepnetworks with a mass of parameters which limits their
realapplications. Thus we also construct a lightweight networkwhich uses
MobileNetV3 as encoder. Experiments show thatthe lightweight network can
perform on par with many largemodels like Monodepth2 at high-resolution with
only20%parameters. All codes and models will be available at
https://github.com/shawLyu/HR-Depth.
