  Mirrors are everywhere in our daily lives. Existing computer vision systems
do not consider mirrors, and hence may get confused by the reflected content
inside a mirror, resulting in a severe performance degradation. However,
separating the real content outside a mirror from the reflected content inside
it is non-trivial. The key challenge is that mirrors typically reflect contents
similar to their surroundings, making it very difficult to differentiate the
two. In this paper, we present a novel method to segment mirrors from an input
image. To the best of our knowledge, this is the first work to address the
mirror segmentation problem with a computational approach. We make the
following contributions. First, we construct a large-scale mirror dataset that
contains mirror images with corresponding manually annotated masks. This
dataset covers a variety of daily life scenes, and will be made publicly
available for future research. Second, we propose a novel network, called
MirrorNet, for mirror segmentation, by modeling both semantical and low-level
color/texture discontinuities between the contents inside and outside of the
mirrors. Third, we conduct extensive experiments to evaluate the proposed
method, and show that it outperforms the carefully chosen baselines from the
state-of-the-art detection and segmentation methods.
