  Mirror detection aims to identify the mirror regions in the given input
image. Existing works mainly focus on integrating the semantic features and
structural features to mine specific relations between mirror and non-mirror
regions, or introducing mirror properties like depth or chirality to help
analyze the existence of mirrors. In this work, we observe that a real object
typically forms a loose symmetry relationship with its corresponding reflection
in the mirror, which is beneficial in distinguishing mirrors from real objects.
Based on this observation, we propose a dual-path Symmetry-Aware
Transformer-based mirror detection Network (SATNet), which includes two novel
modules: Symmetry-Aware Attention Module (SAAM) and Contrast and Fusion Decoder
Module (CFDM). Specifically, we first adopt a transformer backbone to model
global information aggregation in images, extracting multi-scale features in
two paths. We then feed the high-level dual-path features to SAAMs to capture
the symmetry relations. Finally, we fuse the dual-path features and refine our
prediction maps progressively with CFDMs to obtain the final mirror mask.
Experimental results show that SATNet outperforms both RGB and RGB-D mirror
detection methods on all available mirror detection datasets. Codes and trained
models are available at: https://github.com/tyhuang0428/SATNet.
