  Detecting glass regions is a challenging task due to the inherent ambiguity
in their transparency and reflective characteristics. Current solutions in this
field remain rooted in conventional deep learning paradigms, requiring the
construction of annotated datasets and the design of network architectures.
However, the evident drawback with these mainstream solutions lies in the
time-consuming and labor-intensive process of curating datasets, alongside the
increasing complexity of model structures. In this paper, we propose to address
these issues by fully harnessing the capabilities of two existing vision
foundation models (VFMs): Stable Diffusion and Segment Anything Model (SAM).
Firstly, we construct a Synthetic but photorealistic large-scale Glass Surface
Detection dataset, dubbed S-GSD, without any labour cost via Stable Diffusion.
This dataset consists of four different scales, consisting of 168k images
totally with precise masks. Besides, based on the powerful segmentation ability
of SAM, we devise a simple Glass surface sEgMentor named GEM, which follows the
simple query-based encoder-decoder architecture. Comprehensive experiments are
conducted on the large-scale glass segmentation dataset GSD-S. Our GEM
establishes a new state-of-the-art performance with the help of these two VFMs,
surpassing the best-reported method GlassSemNet with an IoU improvement of
2.1%. Additionally, extensive experiments demonstrate that our synthetic
dataset S-GSD exhibits remarkable performance in zero-shot and transfer
learning settings. Codes, datasets and models are publicly available at:
https://github.com/isbrycee/GEM
