Abstract

Glass surfaces appear everywhere. Their existence can however pose a serious problem to computer vision tasks. Recently, a method is proposed to detect glass surfaces by learning multi-scale contextual information. However, as it is only based on a general context integration operation and does not consider any specific glass surface properties, it gets confused when the images contain objects that are similar to glass surfaces and degenerates in challenging scenes with insufficient contexts. We observe that humans often rely on identifying reflections in order to sense the existence of glass and on locating the boundary in order to determine the extent of the glass. Hence, we propose a model for glass surface detection, which consists of two novel modules: (1) a rich context aggregation module (RCAM) to extract multi-scale boundary features from rich context features for locating glass surface boundaries of different sizes and shapes, and (2) a reflection-based refinement module (RRM) to detect reflection and then incorporate it so as to differentiate glass regions from non-glass regions. In addition, we also propose a challenging dataset consisting of 4,012 glass images with annotations for glass surface detection. Our experiments demonstrate that the proposed model outperforms state-of-the-art methods from relevant fields.

Dataset Preview

Citation

 @inproceedings{GSD:2021,
    title={Rich Context Aggregation with Reflection Prior for Glass Surface Detection},
    author={Lin, Jiaying and He, Zebang and Lau, Rynson W.H.},
    booktitle={Proc. CVPR},
    year={2021}
} 

Contact

jiayinlin5-c at my.cityu.edu.hk