Home-Scene-Oriented Glass Segmentation Dataset

Letian Yu,Haiyang Mei,Wen Dong,Ziqi Wei,Li Zhu,Yuxin Wang,Xin Yang.

Progressive Glass Segmentation.

IEEE Transactions on Image Processing (TIP) 2022

https://openaccess.thecvf.com/content/CVPR2022/papers/Mei_Glass_Segmentation_Using_Intensity_and_Spectral_Polarization_Cues_CVPR_2022_paper.pdf

Mei_Glass_Segmentation_Using_Intensity_and_Spectral_Polarization_Cues_CVPR_2022_paper.pdf

Please cite this paper when using the data： BibTex.txt

Introduction:

Glass is very common in the real world . Influenced by the uncertainty about the glass region and the varying complex

scenes behind the glass,the existence of glass poses severe challenges to many computer vision tasks, making glass

segmentation as an important computer vision task. Glass does not have its own visual appearances but only transmit

/reflect the appearances of its surroundings , making it fundamentally different from other common objects. To address 

such a challenging task ,existing methods typically explore and combine useful cues from different levels of features in

the deep network. As there exists a characteristic gap between level-different features, i.e., deep layer features embed

more high-level  semantics and are better at locating the target objects while shallow layer features have larger spatial

sizes and keep richer and more detailed low-level information , fusing these features naively thus would lead to a sub-

optimal solution . In this paper , we approach the effective features fusion towards accurate glass segmentation in two

steps. First, we attempt to bridge the characteristic gap between different levels of features by developing a Discrimin-

ability Enhancement (DE)  module which enables level-specific features to be a more discriminative representation, al-

leviating the features incompatibility for fusion. Second, we design a Focus-and-Exploration Based Fusion (FEBF) mo-

dule to richly excavate useful information in the fusion process by highlighting the common and exploring the differenc-

e between level-different features . Combining these two steps , we construct  a  Progressive Glass Segmentation Net-

work (PGSNet) which uses multiple DE and FEBF modules to progressively aggregate features from high-level to  low-

level ,  implementing a coarse-to-fine glass segmentation. In addition , we build the first home-scene-oriented glass se-

gmentation dataset for advancing household robot applications and in-depth research on this topic. Extensive experim-

ents.