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Glass surfaces of transparent objects and mirrors are not able to be uniquelyand explicitly characterized by their visual appearances because they containthe visual appearance of other reflected or transmitted surfaces as well.Detecting glass regions from a single-color image is a challenging task. Recentdeep-learning approaches have paid attention to the description of glasssurface boundary where the transition of visual appearances between glass andnon-glass surfaces are observed. In this work, we analytically investigate howglass surface boundary helps to characterize glass objects. Inspired by priorsemantic segmentation approaches with challenging image types such as X-ray orCT scans, we propose separated internal-external boundary attention modulesthat individually learn and selectively integrate visual characteristics of theinside and outside region of glass surface from a single color image. Ourproposed method is evaluated on six public benchmarks comparing withstate-of-the-art methods showing promising results.

Internal-External Boundary Attention Fusion for Glass Surface Segmentation