  360{\deg} cameras can capture complete environments in a single shot, which
makes 360{\deg} imagery alluring in many computer vision tasks. However,
monocular depth estimation remains a challenge for 360{\deg} data, particularly
for high resolutions like 2K (2048x1024) and beyond that are important for
novel-view synthesis and virtual reality applications. Current CNN-based
methods do not support such high resolutions due to limited GPU memory. In this
work, we propose a flexible framework for monocular depth estimation from
high-resolution 360{\deg} images using tangent images. We project the 360{\deg}
input image onto a set of tangent planes that produce perspective views, which
are suitable for the latest, most accurate state-of-the-art perspective
monocular depth estimators. To achieve globally consistent disparity estimates,
we recombine the individual depth estimates using deformable multi-scale
alignment followed by gradient-domain blending. The result is a dense,
high-resolution 360{\deg} depth map with a high level of detail, also for
outdoor scenes which are not supported by existing methods. Our source code and
data are available at https://manurare.github.io/360monodepth/.
