Internal-External Boundary Attentions for Transparent Object Segmentation

Recognizing transparent objects such as window and glassware is a challenging task in 3D Reconstruction from color images. In recent years, transparent object recognition methods have focused on feature extraction from boundary region of transparent objects. Our observation is that, unlike non-transparent objects, transparent objects can be characterized and located better by looking at their external and internal boundaries separately. We propose a new internal-external boundaries attention module in which internal and external boundary features are separately recognized. We add an edge-body fully attention module that supervises the segmentation of the generated transparent objects body using semantic information in the external boundaries. We employ contour loss to perform distance-weighted supervision on the inner and outer boundaries separately. Extensive experiments show that proposed method outperforms existing methods on Trans10k dataset.