  In this work, we present an end-to-end network for stereo-consistent image
inpainting with the objective of inpainting large missing regions behind
objects. The proposed model consists of an edge-guided UNet-like network using
Partial Convolutions. We enforce multi-view stereo consistency by introducing a
disparity loss. More importantly, we develop a training scheme where the model
is learned from realistic stereo masks representing object occlusions, instead
of the more common random masks. The technique is trained in a supervised way.
Our evaluation shows competitive results compared to previous state-of-the-art
techniques.
