  Glass-like objects such as windows, bottles, and mirrors exist widely in the
real world. Sensing these objects has many applications, including robot
navigation and grasping. However, this task is very challenging due to the
arbitrary scenes behind glass-like objects. This paper aims to solve the
glass-like object segmentation problem via enhanced boundary learning. In
particular, we first propose a novel refined differential module for generating
finer boundary cues. Then an edge-aware point-based graph convolution network
module is proposed to model the global shape representation along the boundary.
Both modules are lightweight and effective, which can be embedded into various
segmentation models. Moreover, we use these two modules to design a decoder to
get accurate segmentation results, especially on the boundary. Extensive
experiments on three recent glass-like object segmentation datasets, including
Trans10k, MSD, and GDD, show that our approach establishes new state-of-the-art
performances. We also offer the generality and superiority of our approach
compared with recent methods on three general segmentation datasets, including
Cityscapes, BDD, and COCO Stuff. Code and models will be available at
(\url{https://github.com/hehao13/EBLNet})
