  This paper tackles the problem of novel view synthesis from a single image.
In particular, we target real-world scenes with rich geometric structure, a
challenging task due to the large appearance variations of such scenes and the
lack of simple 3D models to represent them. Modern, learning-based approaches
mostly focus on appearance to synthesize novel views and thus tend to generate
predictions that are inconsistent with the underlying scene structure. By
contrast, in this paper, we propose to exploit the 3D geometry of the scene to
synthesize a novel view. Specifically, we approximate a real-world scene by a
fixed number of planes, and learn to predict a set of homographies and their
corresponding region masks to transform the input image into a novel view. To
this end, we develop a new region-aware geometric transform network that
performs these multiple tasks in a common framework. Our results on the outdoor
KITTI and the indoor ScanNet datasets demonstrate the effectiveness of our
network in generating high quality synthetic views that respect the scene
geometry, thus outperforming the state-of-the-art methods.
