Mirror Detection via Multi-Directional Similarity Perception and Spectral Saliency Enhancement

Mirror detection is a challenging task, due to the reflective properties of mirrors. Most existing approaches rely on exploiting the relationship between the content inside the mirror and the surrounding environment to aid in locating mirrors. A typical solution is to utilize contextual contrasted features. However, the discontinuity in content at the edges of mirrors may not always be prominent. To overcome this limitation, we propose a novel mirror detection framework called S² MD including two main modules, multi-directional similarity perception module (MSPM) and spectral saliency enhancement decoder module (SSEDM). Specifically, we employ a backbone network to extract multi-scale global information from images using a dual-path approach. Then, we feed these high-level dual-path features into MSPMs to generate direction-sensitive similarity-consistent features. MSPM utilizes active rotating filters and oriented response pooling to model the similarity relations in different orientations. Moreover, the SSEDM is utilized to enhance the spatial contextual contrasted features using feature spectral residuals and fuse the dual-path features to obtain the final predicted mirror mask. Extensive experiments demonstrate that our method achieves state-of-the-art performance on challenging MSD, PMD, and RGBD-Mirror benchmarks.