  Per-pixel ground-truth depth data is challenging to acquire at scale. To
overcome this limitation, self-supervised learning has emerged as a promising
alternative for training models to perform monocular depth estimation. In this
paper, we propose a set of improvements, which together result in both
quantitatively and qualitatively improved depth maps compared to competing
self-supervised methods.
  Research on self-supervised monocular training usually explores increasingly
complex architectures, loss functions, and image formation models, all of which
have recently helped to close the gap with fully-supervised methods. We show
that a surprisingly simple model, and associated design choices, lead to
superior predictions. In particular, we propose (i) a minimum reprojection
loss, designed to robustly handle occlusions, (ii) a full-resolution
multi-scale sampling method that reduces visual artifacts, and (iii) an
auto-masking loss to ignore training pixels that violate camera motion
assumptions. We demonstrate the effectiveness of each component in isolation,
and show high quality, state-of-the-art results on the KITTI benchmark.
