Glass Makes Blurs: Learning the Visual Blurriness for Glass Surface Detection

Glass surface detection is challenging as glass normally borrows similar visual appearances from the arbitrary objects/scenes behind it. Although some methods have been proposed to address this problem, they may fail if the reference objects are nonexistent or the additional annotations are missing. This article aims to address the glass surface detection problem by utilizing the intrinsic glass properties without reference objects and additional annotations. We observe glass makes blurs naturally. Based on the investigation of this intrinsic visual blurriness cue, we propose a novel visual blurriness aggregation module to model visual blurriness as a learnable residual in order to extract and aggregate multiscale valuable visual blurriness features used for guiding the backbone features to detect glass precisely. Besides, we note the ratio of the blurred area assists in utilizing the visual blurriness cue caused by glass and propose a visual blurriness driven refinement module to refine glass maps with this ratio to better leverage the visual blurriness information. Extensive experiments show that the proposed method achieves state-of-the-art performance on popular glass surface datasets.