  Single view depth estimation models can be trained from video footage using a
self-supervised end-to-end approach with view synthesis as the supervisory
signal. This is achieved with a framework that predicts depth and camera
motion, with a loss based on reconstructing a target video frame from
temporally adjacent frames. In this context, occlusion relates to parts of a
scene that can be observed in the target frame but not in a frame used for
image reconstruction. Since the image reconstruction is based on sampling from
the adjacent frame, and occluded areas by definition cannot be sampled,
reconstructed occluded areas corrupt to the supervisory signal. In previous
work arXiv:1806.01260 occlusion is handled based on reconstruction error; at
each pixel location, only the reconstruction with the lowest error is included
in the loss. The current study aims to determine whether performance
improvements of depth estimation models can be gained by during training only
ignoring those regions that are affected by occlusion.
  In this work we introduce occlusion mask, a mask that during training can be
used to specifically ignore regions that cannot be reconstructed due to
occlusions. Occlusion mask is based entirely on predicted depth information. We
introduce two novel loss formulations which incorporate the occlusion mask. The
method and implementation of arXiv:1806.01260 serves as the foundation for our
modifications as well as the baseline in our experiments. We demonstrate that
(i) incorporating occlusion mask in the loss function improves the performance
of single image depth prediction models on the KITTI benchmark. (ii) loss
functions that select from reconstructions based on error are able to ignore
some of the reprojection error caused by object motion.
