Mirror Detection With the Visual Chirality Cue

Mirror detection is challenging because the visual appearances of mirrors change depending on those of their surroundings. As existing mirror detection methods are mainly based on extracting contextual contrast and relational similarity between mirror and non-mirror regions, they may fail to identify a mirror region if these assumptions are violated. Inspired by a recent study of applying a CNN to help distinguish whether an image is flipped or not based on the visual chirality property, in this paper, we rethink this image-level visual chirality property and reformulate it as a learnable pixel level cue for mirror detection. Specifically, we first propose a novel flipping-convolution-flipping (FCF) transformation to model visual chirality as learnable commutative residual. We then propose a novel visual chirality embedding (VCE) module to exploit this commutative residual in multi-scale feature maps, to embed the visual chirality features into our mirror detection model. Besides, we also propose a visual chirality-guided edge detection (CED) module to integrate the visual chirality features with contextual features for detection refinement. Extensive experiments show that the proposed method outperforms state-of-the-art methods on three benchmark datasets.