Where Is My Mirror?

TL;DR

This paper introduces a novel approach for segmenting mirrors in images, addressing a previously overlooked problem in computer vision that affects system performance by differentiating real content from reflections.

Contribution

It presents the first computational method for mirror segmentation, including a new large-scale dataset and a specialized neural network called MirrorNet.

Findings

MirrorNet outperforms state-of-the-art segmentation methods.

A large-scale annotated mirror dataset is created and made publicly available.

Extensive experiments validate the effectiveness of the proposed approach.

Abstract

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.