  Glass surfaces are becoming increasingly ubiquitous as modern buildings tend
to use a lot of glass panels. This however poses substantial challenges on the
operations of autonomous systems such as robots, self-driving cars and drones,
as the glass panels can become transparent obstacles to the navigation.Existing
works attempt to exploit various cues, including glass boundary context or
reflections, as a prior. However, they are all based on input RGB images.We
observe that the transmission of 3D depth sensor light through glass surfaces
often produces blank regions in the depth maps, which can offer additional
insights to complement the RGB image features for glass surface detection. In
this paper, we propose a novel framework for glass surface detection by
incorporating RGB-D information, with two novel modules: (1) a cross-modal
context mining (CCM) module to adaptively learn individual and mutual context
features from RGB and depth information, and (2) a depth-missing aware
attention (DAA) module to explicitly exploit spatial locations where missing
depths occur to help detect the presence of glass surfaces. In addition, we
propose a large-scale RGB-D glass surface detection dataset, called
\textit{RGB-D GSD}, for RGB-D glass surface detection. Our dataset comprises
3,009 real-world RGB-D glass surface images with precise annotations. Extensive
experimental results show that our proposed model outperforms state-of-the-art
methods.
